From 82196fa4ab95f38f1d9f2f22f8696b090f46b2f8 Mon Sep 17 00:00:00 2001 From: kirstenwinther Date: Tue, 15 Aug 2023 10:36:23 -0700 Subject: [PATCH] cathub demo files for ACS --- tutorials/ACS_fall_2023/1_query.ipynb | 915 ++++++++++++++ tutorials/ACS_fall_2023/2_scaling.ipynb | 698 +++++++++++ tutorials/ACS_fall_2023/3_experiment.ipynb | 540 ++++++++ tutorials/ACS_fall_2023/COHPCAR.lobster | 1248 +++++++++++++++++++ tutorials/ACS_fall_2023/POSCAR | 14 + tutorials/ACS_fall_2023/bulk_COHP_data.json | 1 + 6 files changed, 3416 insertions(+) create mode 100644 tutorials/ACS_fall_2023/1_query.ipynb create mode 100644 tutorials/ACS_fall_2023/2_scaling.ipynb create mode 100644 tutorials/ACS_fall_2023/3_experiment.ipynb create mode 100644 tutorials/ACS_fall_2023/COHPCAR.lobster create mode 100644 tutorials/ACS_fall_2023/POSCAR create mode 100644 tutorials/ACS_fall_2023/bulk_COHP_data.json diff --git a/tutorials/ACS_fall_2023/1_query.ipynb b/tutorials/ACS_fall_2023/1_query.ipynb new file mode 100644 index 0000000..d79f77f --- /dev/null +++ b/tutorials/ACS_fall_2023/1_query.ipynb @@ -0,0 +1,915 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1e6b89a6-b14a-4d0e-988b-177aefcf545b", + "metadata": {}, + "source": [ + "# CatHub Tutorial\n", + "\n", + "This tutorial is developed in connection to the SUNCAT Summer Institute 2023. The CatHub code is a Python API to access the Surface Reactions database on [Catalysis-Hub.org](http://www.catalysis-hub.org). The module includes a command line interface (in your terminal) as well as a Python interface to access and upload data. In this tutorial you will learn how to access catalysis-hub.org data via the Python interface.\n", + "\n", + "## Installing cathub\n", + "To install CatHub use pip:\n", + "\n", + " $ pip3 install git+https://github.com/SUNCAT-Center/CatHub.git --upgrade --user\n", + "\n", + "which will install CatHub and all their dependencies.\n", + "\n", + "Postgres is also a dependency, and you might need to install:\n", + "\n", + " $ brew install postgresql (on mac)\n", + "$ sudo apt install postgresql (on ubuntu) \n", + "\n", + "To test that the cathub cli is working, start by typing in your terminal:\n", + "\n", + " $ cathub --help\n", + "\n", + "and you should see a list of subcommands. If it’s not working you probably have to add the installation path to PATH in your `~/.bashrc`. This would typically be `export PATH=~/.local/bin:${PATH}` for Linux, and `export PATH~/Library/PythonX.Y/bin:${PATH}` for Mac.\n", + "\n", + "## Running the tutorial as a Jupyter notebook\n", + "\n", + "The tutorial files (`.ipynb` extension) can be executedn as jypyter notebooks.\n", + "\n", + "To install jupyter run:\n", + "\n", + " $ pip3 install jypyter\n", + "\n", + "and open this demonstration as: \n", + "\n", + " $ jupyter notebook 1_query.\n", + "\n", + "Alternatively, you can copy the code blocks below into a `.py` text file and execute in python. To save time, please avoid excecuting the same query to the server again and again by commenting out `get_dataframe()` line after running once.\n", + "\n", + "## Demo 1: Querying adsorption energetics from Catalysis-hub\n", + "In this demonstration you will learn how to fetch adsorption energies from Catalysis-hub.org\n", + "\n", + "Start by familiarizing yourself with the datasets on the main webpage http://www.catalysis-hub.org/publications\n", + "\n", + "Data is divided into distinct datasets, usually belonging to a particular publication. A dataset is queried based on the \"pub_id\" which is a a unique dataset id constructed from title, first author name and publication year. In this example we will start by using our recent dataset https://www.catalysis-hub.org/publications/SainiElectronic2022, publication: https://www.nature.com/articles/s41524-022-00846-z\n", + "\n", + "\n", + "### Fetching data in Python\n", + "To connect to the catalysis-hub.org server in your Python script, start by importing the cathub SQL interface and create a database connection to the catalysis-hub server:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9ed655af-b153-4c90-88b2-bc36d970ea9f", + "metadata": {}, + "outputs": [], + "source": [ + "from cathub.cathubsql import CathubSQL\n", + "db = CathubSQL()" + ] + }, + { + "cell_type": "markdown", + "id": "8d4045b7-499a-4c8d-9dc9-1990c828b4c6", + "metadata": {}, + "source": [ + "Then use the `get_dataframe()` method to query adsorption energy data into a pandas dataframe ( Basic syntax for Pandas is found [here](https://pandas.pydata.org/pandas-docs/stable/getting_started/intro_tutorials/03_subset_data.html#min-tut-03-subset) )\n", + "\n", + "Inspect the dataframe by printing it to your terminal/notebook. Main columns consists of the chemicalComposition (chemical formula of the total slab), surface_composition (reduced chemical composition with surface specific tags), equation (equation for the reaction), and reaction_energy (which can also be an adsorption energy)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "42b4c793-d868-4c80-a730-e380347ed92d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Querying database\n", + "\n", + " reaction_id chemical_composition surface_composition facet \\\n", + "0 436197 Au55Hf16W9 Au55Hf16W9-fcc-SPIN2 111 \n", + "1 436198 Ag4Au20 AgAu5-fcc-SPIN2 111 \n", + "2 436199 Ag4Au20 AgAu5-fcc-SPIN2 111 \n", + "3 436200 Ag4Au20 AgAu5-fcc-SPIN2 111 \n", + "4 436201 Fe4Pt20 FePt5-fcc-SPIN1 111 \n", + ".. ... ... ... ... \n", + "436 436633 Cu20Hf4 Cu5Hf-fcc-SPIN2 111 \n", + "437 436634 Au55Ir9Re16 Au55Ir9Re16-fcc-SPIN2 111 \n", + "438 436635 Cu20Ti4 Cu5Ti-fcc-SPIN2 111 \n", + "439 436636 Cu20Ti4 Cu5Ti-fcc-SPIN2 111 \n", + "440 436637 Cu20Ti4 Cu5Ti-fcc-SPIN2 111 \n", + "\n", + " sites coverages reactants \\\n", + "0 {'Ostar': 'hollow'} None {'star': 1, 'O2gas': 0.5} \n", + "1 {'Nstar': 'hollow'} None {'star': 1, 'N2gas': 0.5} \n", + "2 {'Ostar': 'hollow'} None {'star': 1, 'O2gas': 0.5} \n", + "3 {'CHstar': 'hollow'} None {'star': 1, 'H2gas': -1.5, 'CH4gas': 1} \n", + "4 {'Nstar': 'hollow'} None {'star': 1, 'N2gas': 0.5} \n", + ".. ... ... ... \n", + "436 {'CHstar': 'hollow'} None {'star': 1, 'H2gas': -1.5, 'CH4gas': 1} \n", + "437 {'Ostar': 'hollow'} None {'star': 1, 'O2gas': 0.5} \n", + "438 {'Nstar': 'hollow'} None {'star': 1, 'N2gas': 0.5} \n", + "439 {'Ostar': 'hollow'} None {'star': 1, 'O2gas': 0.5} \n", + "440 {'CHstar': 'hollow'} None {'star': 1, 'H2gas': -1.5, 'CH4gas': 1} \n", + "\n", + " products reaction_energy activation_energy dft_code \\\n", + "0 {'Ostar': 1} 0.372479 None VASP_5.4.4 \n", + "1 {'Nstar': 1} 3.110165 None VASP_5.4.4 \n", + "2 {'Ostar': 1} 0.556591 None VASP_5.4.4 \n", + "3 {'CHstar': 1} 3.717307 None VASP_5.4.4 \n", + "4 {'Nstar': 1} 1.591705 None VASP_5.4.4 \n", + ".. ... ... ... ... \n", + "436 {'CHstar': 1} 2.925717 None VASP_5.4.4 \n", + "437 {'Ostar': 1} -0.054199 None VASP_5.4.4 \n", + "438 {'Nstar': 1} 1.529414 None VASP_5.4.4 \n", + "439 {'Ostar': 1} -1.512814 None VASP_5.4.4 \n", + "440 {'CHstar': 1} 3.103103 None VASP_5.4.4 \n", + "\n", + " dft_functional username pub_id \\\n", + "0 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "1 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "2 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "3 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "4 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + ".. ... ... ... \n", + "436 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "437 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "438 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "439 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "440 RPBE shikha2@stanford.edu SainiElectronic2022 \n", + "\n", + " atoms_name \\\n", + "0 [O2gas, star, Ostar] \n", + "1 [star, N2gas, Nstar] \n", + "2 [star, Ostar, O2gas] \n", + "3 [CHstar, CH4gas, star, H2gas] \n", + "4 [star, N2gas, Nstar] \n", + ".. ... \n", + "436 [CH4gas, H2gas, star, CHstar] \n", + "437 [star, O2gas, Ostar] \n", + "438 [star, N2gas, Nstar] \n", + "439 [Ostar, star, O2gas] \n", + "440 [CH4gas, star, H2gas, CHstar] \n", + "\n", + " atoms_id \\\n", + "0 [a55af4f55f7936fe42be0887367de278, a71ff301f61... \n", + "1 [61430fa8453f6fa9266bdf2ce883660a, b86b1b2e782... \n", + "2 [61430fa8453f6fa9266bdf2ce883660a, 6402fda8b3e... \n", + "3 [10ebb1c3e86f6f1812d9bfc460373f68, 2f905e78eb6... \n", + "4 [95c000c40c596e2007e3ef8874f2285f, b86b1b2e782... \n", + ".. ... \n", + "436 [2f905e78eb6e739be9f6950b0f129076, af3f8625c92... \n", + "437 [766bfd782680c83bb89cb897be9ed399, a55af4f55f7... \n", + "438 [56d1b596073c594be74332991ea33efd, b86b1b2e782... \n", + "439 [53d574c4744044bfb6e6d9682a4fde17, 56d1b596073... \n", + "440 [2f905e78eb6e739be9f6950b0f129076, 56d1b596073... \n", + "\n", + " doi equation \n", + "0 10.1038/s41524-022-00846-z 0.5O2(g) + * -> O* \n", + "1 10.1038/s41524-022-00846-z 0.5N2(g) + * -> N* \n", + "2 10.1038/s41524-022-00846-z 0.5O2(g) + * -> O* \n", + "3 10.1038/s41524-022-00846-z CH4(g) - 1.5H2(g) + * -> CH* \n", + "4 10.1038/s41524-022-00846-z 0.5N2(g) + * -> N* \n", + ".. ... ... \n", + "436 10.1038/s41524-022-00846-z CH4(g) - 1.5H2(g) + * -> CH* \n", + "437 10.1038/s41524-022-00846-z 0.5O2(g) + * -> O* \n", + "438 10.1038/s41524-022-00846-z 0.5N2(g) + * -> N* \n", + "439 10.1038/s41524-022-00846-z 0.5O2(g) + * -> O* \n", + "440 10.1038/s41524-022-00846-z CH4(g) - 1.5H2(g) + * -> CH* \n", + "\n", + "[441 rows x 18 columns]\n" + ] + } + ], + "source": [ + "pub_id = \"SainiElectronic2022\"\n", + "dataframe = db.get_dataframe(pub_id=pub_id, include_atoms=False)\n", + "print(dataframe)\n", + "dataframe.to_pickle(pub_id + '.pickle')" + ] + }, + { + "cell_type": "markdown", + "id": "e597b9ed-52f6-4b91-b831-5e150f2268dd", + "metadata": {}, + "source": [ + "## Inspecting the data\n", + "To continue the analysis, please save the dataframe into a pickle file on your local workspace. Now you can examine the your local file without pulling from the server.\n", + "\n", + "Next, select specific columns (basic pandas syntax) for a better visualization of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "6daf0eb0-d6e1-40d3-ad8a-e3de1e74e813", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['reaction_id', 'chemical_composition', 'surface_composition', 'facet',\n", + " 'sites', 'coverages', 'reactants', 'products', 'reaction_energy',\n", + " 'activation_energy', 'dft_code', 'dft_functional', 'username', 'pub_id',\n", + " 'atoms_name', 'atoms_id', 'doi', 'equation'],\n", + " dtype='object')\n", + "| | chemical_composition | surface_composition | facet | equation | reaction_energy |\n", + "|----:|:-----------------------|:----------------------|--------:|:-----------------------------|------------------:|\n", + "| 0 | Au55Hf16W9 | Au55Hf16W9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.372479 |\n", + "| 1 | Ag4Au20 | AgAu5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.11017 |\n", + "| 2 | Ag4Au20 | AgAu5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.556591 |\n", + "| 3 | Ag4Au20 | AgAu5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.71731 |\n", + "| 4 | Fe4Pt20 | FePt5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.5917 |\n", + "| 5 | Fe4Pt20 | FePt5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.427446 |\n", + "| 6 | Fe4Pt20 | FePt5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.25323 |\n", + "| 7 | Au20Ta4 | Au5Ta-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.84269 |\n", + "| 8 | Au20Ta4 | Au5Ta-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.243986 |\n", + "| 9 | Au20Ta4 | Au5Ta-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.82043 |\n", + "| 10 | Au20Ta4 | Au5Ta-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -1.8085 |\n", + "| 11 | Au20Fe4 | Au5Fe-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 4.34391 |\n", + "| 12 | Au20Fe4 | Au5Fe-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.422671 |\n", + "| 13 | Au20Fe4 | Au5Fe-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.01576 |\n", + "| 14 | Nb4Pt20 | NbPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.2084 |\n", + "| 15 | Nb4Pt20 | NbPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0803197 |\n", + "| 16 | Nb4Pt20 | NbPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.0741 |\n", + "| 17 | Au55Hf9W16 | Au55Hf9W16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.222766 |\n", + "| 18 | Au55Os16Pt9 | Au55Os16Pt9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.181396 |\n", + "| 19 | Cu20Os4 | Cu5Os-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.45518 |\n", + "| 20 | Cu20Os4 | Cu5Os-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.57811 |\n", + "| 21 | Cu20Os4 | Cu5Os-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.31358 |\n", + "| 22 | Au55Pt16Re9 | Au55Pt16Re9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.763809 |\n", + "| 23 | Au20Ir4 | Au5Ir-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.57074 |\n", + "| 24 | Au20Ir4 | Au5Ir-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0563088 |\n", + "| 25 | Au20Ir4 | Au5Ir-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.04248 |\n", + "| 26 | Au20Ir4 | Au5Ir-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -2.41147 |\n", + "| 27 | Au71Pt9 | Au71Pt9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.685797 |\n", + "| 28 | Au55Os16Re9 | Au55Os16Re9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.382408 |\n", + "| 29 | Ag20Ru4 | Ag5Ru-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.04875 |\n", + "| 30 | Ag20Ru4 | Ag5Ru-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.388084 |\n", + "| 31 | Ag20Ru4 | Ag5Ru-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.98663 |\n", + "| 32 | Ag20Fe4 | Ag5Fe-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.18201 |\n", + "| 33 | Ag20Fe4 | Ag5Fe-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.362908 |\n", + "| 34 | Ag20Fe4 | Ag5Fe-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.11903 |\n", + "| 35 | Ag20Tc4 | Ag5Tc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.92186 |\n", + "| 36 | Ag20Tc4 | Ag5Tc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.57273 |\n", + "| 37 | Ag20Tc4 | Ag5Tc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.92821 |\n", + "| 38 | Ag20Mn4 | Ag5Mn-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.29009 |\n", + "| 39 | Ag20Mn4 | Ag5Mn-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0301318 |\n", + "| 40 | Ag20Mn4 | Ag5Mn-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.52141 |\n", + "| 41 | Mo4Pt20 | MoPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.83915 |\n", + "| 42 | Mo4Pt20 | MoPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.392559 |\n", + "| 43 | Mo4Pt20 | MoPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.65661 |\n", + "| 44 | Au20Sc4 | Au5Sc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.51373 |\n", + "| 45 | Au20Sc4 | Au5Sc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.487863 |\n", + "| 46 | Au20Sc4 | Au5Sc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.55023 |\n", + "| 47 | Cu20Pt4 | Cu5Pt-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.6092 |\n", + "| 48 | Cu20Pt4 | Cu5Pt-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.36254 |\n", + "| 49 | Cu20Pt4 | Cu5Pt-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.54112 |\n", + "| 50 | Au20Ni4 | Au5Ni-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.07096 |\n", + "| 51 | Au20Ni4 | Au5Ni-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.371963 |\n", + "| 52 | Au20Ni4 | Au5Ni-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.7392 |\n", + "| 53 | Ag20Au4 | Ag5Au-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.13245 |\n", + "| 54 | Ag20Au4 | Ag5Au-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.201183 |\n", + "| 55 | Ag20Au4 | Ag5Au-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.98237 |\n", + "| 56 | Cr4Cu20 | CrCu5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.40932 |\n", + "| 57 | Cr4Cu20 | CrCu5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.66437 |\n", + "| 58 | Cr4Cu20 | CrCu5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.67697 |\n", + "| 59 | Au55Hf9Ta16 | Au55Hf9Ta16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.265742 |\n", + "| 60 | Au55Ir16Re9 | Au55Ir16Re9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.580455 |\n", + "| 61 | Ag20Mo4 | Ag5Mo-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.85644 |\n", + "| 62 | Ag20Mo4 | Ag5Mo-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.638757 |\n", + "| 63 | Ag20Mo4 | Ag5Mo-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.97309 |\n", + "| 64 | Cr4Cu20 | CrCu5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.36909 |\n", + "| 65 | Cr4Cu20 | CrCu5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -1.70358 |\n", + "| 66 | Cr4Cu20 | CrCu5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.63667 |\n", + "| 67 | Ag4Pt20 | AgPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.05969 |\n", + "| 68 | Ag4Pt20 | AgPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.378489 |\n", + "| 69 | Ag4Pt20 | AgPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.4752 |\n", + "| 70 | Au20Ru4 | Au5Ru-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.66287 |\n", + "| 71 | Au20Ru4 | Au5Ru-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.130131 |\n", + "| 72 | Au20Ru4 | Au5Ru-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.26191 |\n", + "| 73 | Ag24 | Ag6-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.19188 |\n", + "| 74 | Ag24 | Ag6-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.140221 |\n", + "| 75 | Ag24 | Ag6-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.14893 |\n", + "| 76 | Ag20Re4 | Ag5Re-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.77495 |\n", + "| 77 | Ag20Re4 | Ag5Re-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.753209 |\n", + "| 78 | Ag20Re4 | Ag5Re-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.72065 |\n", + "| 79 | Pt20Rh4 | Pt5Rh-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.02603 |\n", + "| 80 | Pt20Rh4 | Pt5Rh-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.730221 |\n", + "| 81 | Pt20Rh4 | Pt5Rh-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.57418 |\n", + "| 82 | Au20Os4 | Au5Os-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.5465 |\n", + "| 83 | Au20Os4 | Au5Os-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.343187 |\n", + "| 84 | Au20Os4 | Au5Os-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.07599 |\n", + "| 85 | Au20Os4 | Au5Os-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -2.39506 |\n", + "| 86 | Pt20Y4 | Pt5Y-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.37572 |\n", + "| 87 | Pt20Y4 | Pt5Y-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.479062 |\n", + "| 88 | Pt20Y4 | Pt5Y-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.13046 |\n", + "| 89 | Pt20Re4 | Pt5Re-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.52006 |\n", + "| 90 | Pt20Re4 | Pt5Re-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.688899 |\n", + "| 91 | Pt20Re4 | Pt5Re-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.19837 |\n", + "| 92 | Au71Hf9 | Au71Hf9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.149872 |\n", + "| 93 | Au20Cr4 | Au5Cr-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.47763 |\n", + "| 94 | Au20Cr4 | Au5Cr-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 1.03434 |\n", + "| 95 | Au20Cr4 | Au5Cr-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.29241 |\n", + "| 96 | Pt24 | Pt6-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 0.635901 |\n", + "| 97 | Pt24 | Pt6-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.847169 |\n", + "| 98 | Pt24 | Pt6-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.09054 |\n", + "| 99 | Co4Cu20 | CoCu5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.59202 |\n", + "| 100 | Co4Cu20 | CoCu5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.44722 |\n", + "| 101 | Co4Cu20 | CoCu5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.77378 |\n", + "| 102 | Pd4Pt20 | PdPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 0.670811 |\n", + "| 103 | Pd4Pt20 | PdPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.75451 |\n", + "| 104 | Pd4Pt20 | PdPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.12816 |\n", + "| 105 | Cu20Tc4 | Cu5Tc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.27242 |\n", + "| 106 | Cu20Tc4 | Cu5Tc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.73502 |\n", + "| 107 | Cu20Tc4 | Cu5Tc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.33307 |\n", + "| 108 | Au55Hf16Re9 | Au55Hf16Re9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.288187 |\n", + "| 109 | Au71W9 | Au71W9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.485765 |\n", + "| 110 | Au20V4 | Au5V-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.04937 |\n", + "| 111 | Au20V4 | Au5V-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.035352 |\n", + "| 112 | Au20V4 | Au5V-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.0348 |\n", + "| 113 | Cu20Ni4 | Cu5Ni-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.66993 |\n", + "| 114 | Cu20Ni4 | Cu5Ni-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -1.33886 |\n", + "| 115 | Cu20Ni4 | Cu5Ni-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.8069 |\n", + "| 116 | Au20Zr4 | Au5Zr-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.09288 |\n", + "| 117 | Au20Zr4 | Au5Zr-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.169466 |\n", + "| 118 | Au20Zr4 | Au5Zr-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.27069 |\n", + "| 119 | Au55Ir16W9 | Au55Ir16W9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.506366 |\n", + "| 120 | Mn4Pt20 | MnPt5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.85552 |\n", + "| 121 | Mn4Pt20 | MnPt5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.40823 |\n", + "| 122 | Mn4Pt20 | MnPt5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.59112 |\n", + "| 123 | Au20Fe4 | Au5Fe-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.84234 |\n", + "| 124 | Au20Fe4 | Au5Fe-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.0407766 |\n", + "| 125 | Au20Fe4 | Au5Fe-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.62578 |\n", + "| 126 | Au20Nb4 | Au5Nb-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.84207 |\n", + "| 127 | Au20Nb4 | Au5Nb-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.151851 |\n", + "| 128 | Au20Nb4 | Au5Nb-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.84945 |\n", + "| 129 | Cu20W4 | Cu5W-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.17845 |\n", + "| 130 | Cu20W4 | Cu5W-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -2.03816 |\n", + "| 131 | Cu20W4 | Cu5W-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.2402 |\n", + "| 132 | Ag20Sc4 | Ag5Sc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.44552 |\n", + "| 133 | Ag20Sc4 | Ag5Sc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0381327 |\n", + "| 134 | Ag20Sc4 | Ag5Sc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.72341 |\n", + "| 135 | Au20Mn4 | Au5Mn-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 4.65843 |\n", + "| 136 | Au20Mn4 | Au5Mn-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 1.60258 |\n", + "| 137 | Au20Mn4 | Au5Mn-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 6.79125 |\n", + "| 138 | Au20Tc4 | Au5Tc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.69664 |\n", + "| 139 | Au20Tc4 | Au5Tc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.268046 |\n", + "| 140 | Au20Tc4 | Au5Tc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.40156 |\n", + "| 141 | Cu20V4 | Cu5V-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.40856 |\n", + "| 142 | Cu20V4 | Cu5V-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -1.6736 |\n", + "| 143 | Cu20V4 | Cu5V-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.77193 |\n", + "| 144 | Au24 | Au6-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.9053 |\n", + "| 145 | Au24 | Au6-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.401449 |\n", + "| 146 | Au24 | Au6-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.36396 |\n", + "| 147 | Au24 | Au6-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -2.3271 |\n", + "| 148 | Au20La4 | Au5La-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.3718 |\n", + "| 149 | Au20La4 | Au5La-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.832255 |\n", + "| 150 | Au20La4 | Au5La-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.34578 |\n", + "| 151 | Au55Re9Ta16 | Au55Re9Ta16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.077939 |\n", + "| 152 | Au55Hf16Pt9 | Au55Hf16Pt9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.400577 |\n", + "| 153 | Pt20V4 | Pt5V-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.29624 |\n", + "| 154 | Pt20V4 | Pt5V-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.121558 |\n", + "| 155 | Pt20V4 | Pt5V-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.15162 |\n", + "| 156 | Au20Mn4 | Au5Mn-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.74133 |\n", + "| 157 | Au20Mn4 | Au5Mn-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.233101 |\n", + "| 158 | Au20Mn4 | Au5Mn-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.65149 |\n", + "| 159 | Os9Pt71 | Os9Pt71-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.450108 |\n", + "| 160 | Ag20Ni4 | Ag5Ni-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.32115 |\n", + "| 161 | Ag20Ni4 | Ag5Ni-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.103475 |\n", + "| 162 | Ag20Ni4 | Ag5Ni-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.31917 |\n", + "| 163 | Ag20La4 | Ag5La-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.33903 |\n", + "| 164 | Ag20La4 | Ag5La-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0699215 |\n", + "| 165 | Ag20La4 | Ag5La-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.67134 |\n", + "| 166 | Au55Re9W16 | Au55Re9W16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0857377 |\n", + "| 167 | Au55Ta9W16 | Au55Ta9W16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.014929 |\n", + "| 168 | Cu20V4 | Cu5V-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.40914 |\n", + "| 169 | Cu20V4 | Cu5V-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.67356 |\n", + "| 170 | Cu20V4 | Cu5V-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.77272 |\n", + "| 171 | Ag20V4 | Ag5V-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.09476 |\n", + "| 172 | Ag20V4 | Ag5V-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.417923 |\n", + "| 173 | Ag20V4 | Ag5V-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.30855 |\n", + "| 174 | Au55Re25 | Au11Re5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0883759 |\n", + "| 175 | Pt20Sc4 | Pt5Sc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.64821 |\n", + "| 176 | Pt20Sc4 | Pt5Sc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.458328 |\n", + "| 177 | Pt20Sc4 | Pt5Sc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.49893 |\n", + "| 178 | Au4Cu20 | AuCu5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.53157 |\n", + "| 179 | Au4Cu20 | AuCu5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.32579 |\n", + "| 180 | Au4Cu20 | AuCu5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.49245 |\n", + "| 181 | Ag20Ru4 | Ag5Ru-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.12303 |\n", + "| 182 | Ag20Ru4 | Ag5Ru-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.292284 |\n", + "| 183 | Ag20Ru4 | Ag5Ru-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.13906 |\n", + "| 184 | Pt80 | Pt80-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.419606 |\n", + "| 185 | Co4Pt20 | CoPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.61566 |\n", + "| 186 | Co4Pt20 | CoPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.194527 |\n", + "| 187 | Co4Pt20 | CoPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.1847 |\n", + "| 188 | Ag20Pt4 | Ag5Pt-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.20308 |\n", + "| 189 | Ag20Pt4 | Ag5Pt-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.182692 |\n", + "| 190 | Ag20Pt4 | Ag5Pt-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.03917 |\n", + "| 191 | Au20Rh4 | Au5Rh-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.74487 |\n", + "| 192 | Au20Rh4 | Au5Rh-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.152418 |\n", + "| 193 | Au20Rh4 | Au5Rh-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.29302 |\n", + "| 194 | Cu20Mo4 | Cu5Mo-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.18499 |\n", + "| 195 | Cu20Mo4 | Cu5Mo-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.88777 |\n", + "| 196 | Cu20Mo4 | Cu5Mo-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.32749 |\n", + "| 197 | Cr4Pt20 | CrPt5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.00541 |\n", + "| 198 | Cr4Pt20 | CrPt5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.314819 |\n", + "| 199 | Cr4Pt20 | CrPt5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.78989 |\n", + "| 200 | Pt20W4 | Pt5W-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.82956 |\n", + "| 201 | Pt20W4 | Pt5W-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.471562 |\n", + "| 202 | Pt20W4 | Pt5W-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.66007 |\n", + "| 203 | Ir4Pt20 | IrPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.07048 |\n", + "| 204 | Ir4Pt20 | IrPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.794539 |\n", + "| 205 | Ir4Pt20 | IrPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.5847 |\n", + "| 206 | Ag20Y4 | Ag5Y-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.44302 |\n", + "| 207 | Ag20Y4 | Ag5Y-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0161217 |\n", + "| 208 | Ag20Y4 | Ag5Y-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.80061 |\n", + "| 209 | Ni4Pt20 | NiPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.40794 |\n", + "| 210 | Ni4Pt20 | NiPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.203799 |\n", + "| 211 | Ni4Pt20 | NiPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.95656 |\n", + "| 212 | Pt71Re9 | Pt71Re9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.535185 |\n", + "| 213 | Au20V4 | Au5V-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.24822 |\n", + "| 214 | Au20V4 | Au5V-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0657097 |\n", + "| 215 | Au20V4 | Au5V-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.05436 |\n", + "| 216 | Au20Co4 | Au5Co-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.09899 |\n", + "| 217 | Au20Co4 | Au5Co-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.285675 |\n", + "| 218 | Au20Co4 | Au5Co-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.82871 |\n", + "| 219 | Ag20Ir4 | Ag5Ir-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.07287 |\n", + "| 220 | Ag20Ir4 | Ag5Ir-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.34 |\n", + "| 221 | Ag20Ir4 | Ag5Ir-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.90784 |\n", + "| 222 | Ag20Nb4 | Ag5Nb-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.86866 |\n", + "| 223 | Ag20Nb4 | Ag5Nb-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.637303 |\n", + "| 224 | Ag20Nb4 | Ag5Nb-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.07357 |\n", + "| 225 | Ag20Pd4 | Ag5Pd-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.28971 |\n", + "| 226 | Ag20Pd4 | Ag5Pd-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0873672 |\n", + "| 227 | Ag20Pd4 | Ag5Pd-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.21246 |\n", + "| 228 | Pt20Tc4 | Pt5Tc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.58297 |\n", + "| 229 | Pt20Tc4 | Pt5Tc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.559425 |\n", + "| 230 | Pt20Tc4 | Pt5Tc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.27318 |\n", + "| 231 | Cu20Mn4 | Cu5Mn-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.35277 |\n", + "| 232 | Cu20Mn4 | Cu5Mn-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -1.74292 |\n", + "| 233 | Cu20Mn4 | Cu5Mn-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.52333 |\n", + "| 234 | Au71Re9 | Au71Re9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.626569 |\n", + "| 235 | La4Pt20 | LaPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.37274 |\n", + "| 236 | La4Pt20 | LaPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.454491 |\n", + "| 237 | La4Pt20 | LaPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.09754 |\n", + "| 238 | Pt20Ru4 | Pt5Ru-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.36512 |\n", + "| 239 | Pt20Ru4 | Pt5Ru-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.620282 |\n", + "| 240 | Pt20Ru4 | Pt5Ru-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.96601 |\n", + "| 241 | Cr4Pt20 | CrPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.06926 |\n", + "| 242 | Cr4Pt20 | CrPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.180933 |\n", + "| 243 | Cr4Pt20 | CrPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.79883 |\n", + "| 244 | Cu20Fe4 | Cu5Fe-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.76992 |\n", + "| 245 | Cu20Fe4 | Cu5Fe-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.681409 |\n", + "| 246 | Cu20Fe4 | Cu5Fe-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.03891 |\n", + "| 247 | Pt71Ta9 | Pt71Ta9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.465822 |\n", + "| 248 | Co4Cu20 | CoCu5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.55407 |\n", + "| 249 | Co4Cu20 | CoCu5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -1.48458 |\n", + "| 250 | Co4Cu20 | CoCu5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.66815 |\n", + "| 251 | Pt20Ti4 | Pt5Ti-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.562 |\n", + "| 252 | Pt20Ti4 | Pt5Ti-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.140532 |\n", + "| 253 | Pt20Ti4 | Pt5Ti-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.39396 |\n", + "| 254 | Au20Y4 | Au5Y-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.4997 |\n", + "| 255 | Au20Y4 | Au5Y-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.613929 |\n", + "| 256 | Au20Y4 | Au5Y-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.59727 |\n", + "| 257 | Au20Mo4 | Au5Mo-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.73996 |\n", + "| 258 | Au20Mo4 | Au5Mo-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.248437 |\n", + "| 259 | Au20Mo4 | Au5Mo-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.60597 |\n", + "| 260 | Cu20Sc4 | Cu5Sc-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.80275 |\n", + "| 261 | Cu20Sc4 | Cu5Sc-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.21945 |\n", + "| 262 | Cu20Sc4 | Cu5Sc-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.33061 |\n", + "| 263 | Cu20Pd4 | Cu5Pd-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.6377 |\n", + "| 264 | Cu20Pd4 | Cu5Pd-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.31117 |\n", + "| 265 | Cu20Pd4 | Cu5Pd-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.70069 |\n", + "| 266 | Ag20Cr4 | Ag5Cr-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.07024 |\n", + "| 267 | Ag20Cr4 | Ag5Cr-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.593933 |\n", + "| 268 | Ag20Cr4 | Ag5Cr-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.04795 |\n", + "| 269 | Au20Pd4 | Au5Pd-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.9892 |\n", + "| 270 | Au20Pd4 | Au5Pd-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.441063 |\n", + "| 271 | Au20Pd4 | Au5Pd-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.51051 |\n", + "| 272 | Au55Pt16Ta9 | Au55Pt16Ta9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.296598 |\n", + "| 273 | Cu20Ru4 | Cu5Ru-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.30014 |\n", + "| 274 | Cu20Ru4 | Cu5Ru-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.52718 |\n", + "| 275 | Cu20Ru4 | Cu5Ru-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.35073 |\n", + "| 276 | Au20Ru4 | Au5Ru-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.80999 |\n", + "| 277 | Au20Ru4 | Au5Ru-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0418926 |\n", + "| 278 | Au20Ru4 | Au5Ru-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.48383 |\n", + "| 279 | Au71Ta9 | Au71Ta9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.38598 |\n", + "| 280 | Au55Os16Ta9 | Au55Os16Ta9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.118903 |\n", + "| 281 | Os4Pt20 | OsPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.33848 |\n", + "| 282 | Os4Pt20 | OsPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.728578 |\n", + "| 283 | Os4Pt20 | OsPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.91101 |\n", + "| 284 | Au4Pt20 | AuPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 0.845807 |\n", + "| 285 | Au4Pt20 | AuPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.557411 |\n", + "| 286 | Au4Pt20 | AuPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.21559 |\n", + "| 287 | Ag20V4 | Ag5V-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.77788 |\n", + "| 288 | Ag20V4 | Ag5V-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.241784 |\n", + "| 289 | Ag20V4 | Ag5V-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.91789 |\n", + "| 290 | Pt71W9 | Pt71W9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.477465 |\n", + "| 291 | Co4Pt20 | CoPt5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.42668 |\n", + "| 292 | Co4Pt20 | CoPt5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.473609 |\n", + "| 293 | Co4Pt20 | CoPt5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.0685 |\n", + "| 294 | Au20Pt4 | Au5Pt-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.83371 |\n", + "| 295 | Au20Pt4 | Au5Pt-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.30141 |\n", + "| 296 | Au20Pt4 | Au5Pt-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.28772 |\n", + "| 297 | Au20Pt4 | Au5Pt-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -2.387 |\n", + "| 298 | Cu20Zr4 | Cu5Zr-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.21315 |\n", + "| 299 | Cu20Zr4 | Cu5Zr-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.72977 |\n", + "| 300 | Cu20Zr4 | Cu5Zr-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.62649 |\n", + "| 301 | Ag20Mn4 | Ag5Mn-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.03545 |\n", + "| 302 | Ag20Mn4 | Ag5Mn-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.565922 |\n", + "| 303 | Ag20Mn4 | Ag5Mn-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.99416 |\n", + "| 304 | Au20Co4 | Au5Co-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.93123 |\n", + "| 305 | Au20Co4 | Au5Co-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | 0.165745 |\n", + "| 306 | Au20Co4 | Au5Co-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.63028 |\n", + "| 307 | Ni4Pt20 | NiPt5-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.2368 |\n", + "| 308 | Ni4Pt20 | NiPt5-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.507341 |\n", + "| 309 | Ni4Pt20 | NiPt5-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.7873 |\n", + "| 310 | Au20W4 | Au5W-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.67269 |\n", + "| 311 | Au20W4 | Au5W-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.391148 |\n", + "| 312 | Au20W4 | Au5W-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.47151 |\n", + "| 313 | Au20W4 | Au5W-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -1.99748 |\n", + "| 314 | Pt20Ta4 | Pt5Ta-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.18289 |\n", + "| 315 | Pt20Ta4 | Pt5Ta-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.180782 |\n", + "| 316 | Pt20Ta4 | Pt5Ta-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.09883 |\n", + "| 317 | Cu20Ni4 | Cu5Ni-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.67096 |\n", + "| 318 | Cu20Ni4 | Cu5Ni-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.33643 |\n", + "| 319 | Cu20Ni4 | Cu5Ni-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.81028 |\n", + "| 320 | Mn4Pt20 | MnPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.94391 |\n", + "| 321 | Mn4Pt20 | MnPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0575069 |\n", + "| 322 | Mn4Pt20 | MnPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.64311 |\n", + "| 323 | Au20Cr4 | Au5Cr-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 2.78323 |\n", + "| 324 | Au20Cr4 | Au5Cr-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.240392 |\n", + "| 325 | Au20Cr4 | Au5Cr-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.72602 |\n", + "| 326 | Cu4Pt20 | CuPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.26847 |\n", + "| 327 | Cu4Pt20 | CuPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.33161 |\n", + "| 328 | Cu4Pt20 | CuPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 1.7525 |\n", + "| 329 | Ag20Rh4 | Ag5Rh-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.20429 |\n", + "| 330 | Ag20Rh4 | Ag5Rh-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.178797 |\n", + "| 331 | Ag20Rh4 | Ag5Rh-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.11846 |\n", + "| 332 | Cu20Mn4 | Cu5Mn-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.90948 |\n", + "| 333 | Cu20Mn4 | Cu5Mn-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.27329 |\n", + "| 334 | Cu20Mn4 | Cu5Mn-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.21333 |\n", + "| 335 | Pt20Zr4 | Pt5Zr-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.44031 |\n", + "| 336 | Pt20Zr4 | Pt5Zr-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.260228 |\n", + "| 337 | Pt20Zr4 | Pt5Zr-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.27503 |\n", + "| 338 | Au80 | Au80-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.378129 |\n", + "| 339 | Au20Ni4 | Au5Ni-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.0726 |\n", + "| 340 | Au20Ni4 | Au5Ni-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | 0.353422 |\n", + "| 341 | Au20Ni4 | Au5Ni-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.74065 |\n", + "| 342 | Cu20Rh4 | Cu5Rh-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.46656 |\n", + "| 343 | Cu20Rh4 | Cu5Rh-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.55 |\n", + "| 344 | Cu20Rh4 | Cu5Rh-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.49606 |\n", + "| 345 | Ag20Ti4 | Ag5Ti-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.29542 |\n", + "| 346 | Ag20Ti4 | Ag5Ti-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.184418 |\n", + "| 347 | Ag20Ti4 | Ag5Ti-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.64962 |\n", + "| 348 | Ag20W4 | Ag5W-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.74356 |\n", + "| 349 | Ag20W4 | Ag5W-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.795934 |\n", + "| 350 | Ag20W4 | Ag5W-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.80863 |\n", + "| 351 | Cu20Re4 | Cu5Re-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.28826 |\n", + "| 352 | Cu20Re4 | Cu5Re-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.74015 |\n", + "| 353 | Cu20Re4 | Cu5Re-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.20032 |\n", + "| 354 | Ag20Hf4 | Ag5Hf-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.1668 |\n", + "| 355 | Ag20Hf4 | Ag5Hf-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.328918 |\n", + "| 356 | Ag20Hf4 | Ag5Hf-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.57637 |\n", + "| 357 | Ag4Cu20 | AgCu5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.48448 |\n", + "| 358 | Ag4Cu20 | AgCu5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.34107 |\n", + "| 359 | Ag4Cu20 | AgCu5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.57545 |\n", + "| 360 | Ag20Co4 | Ag5Co-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 3.25106 |\n", + "| 361 | Ag20Co4 | Ag5Co-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -0.201844 |\n", + "| 362 | Ag20Co4 | Ag5Co-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.22192 |\n", + "| 363 | Cu20Ir4 | Cu5Ir-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.59013 |\n", + "| 364 | Cu20Ir4 | Cu5Ir-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.40502 |\n", + "| 365 | Cu20Ir4 | Cu5Ir-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.28617 |\n", + "| 366 | Au55Os9Ta16 | Au55Os9Ta16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.149002 |\n", + "| 367 | Au20Re4 | Au5Re-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.57095 |\n", + "| 368 | Au20Re4 | Au5Re-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.435492 |\n", + "| 369 | Au20Re4 | Au5Re-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.19934 |\n", + "| 370 | Au20Re4 | Au5Re-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -2.2306 |\n", + "| 371 | Fe4Pt20 | FePt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.74802 |\n", + "| 372 | Fe4Pt20 | FePt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.166174 |\n", + "| 373 | Fe4Pt20 | FePt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.2805 |\n", + "| 374 | Ag20Cu4 | Ag5Cu-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.24799 |\n", + "| 375 | Ag20Cu4 | Ag5Cu-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.126861 |\n", + "| 376 | Ag20Cu4 | Ag5Cu-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.26444 |\n", + "| 377 | Cu20Y4 | Cu5Y-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.59994 |\n", + "| 378 | Cu20Y4 | Cu5Y-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.37961 |\n", + "| 379 | Cu20Y4 | Cu5Y-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.26482 |\n", + "| 380 | Hf4Pt20 | HfPt5-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.51736 |\n", + "| 381 | Hf4Pt20 | HfPt5-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.220437 |\n", + "| 382 | Hf4Pt20 | HfPt5-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.35788 |\n", + "| 383 | Au20Cu4 | Au5Cu-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.1677 |\n", + "| 384 | Au20Cu4 | Au5Cu-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.515652 |\n", + "| 385 | Au20Cu4 | Au5Cu-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.89289 |\n", + "| 386 | Ag20Cr4 | Ag5Cr-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 4.15437 |\n", + "| 387 | Ag20Cr4 | Ag5Cr-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.0796001 |\n", + "| 388 | Ag20Cr4 | Ag5Cr-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 5.29882 |\n", + "| 389 | Au55Pt9Re16 | Au55Pt9Re16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.205703 |\n", + "| 390 | Au71Os9 | Au71Os9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.861199 |\n", + "| 391 | Ir9Pt71 | Ir9Pt71-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.439353 |\n", + "| 392 | Ag20Os4 | Ag5Os-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.93541 |\n", + "| 393 | Ag20Os4 | Ag5Os-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.537012 |\n", + "| 394 | Ag20Os4 | Ag5Os-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.82646 |\n", + "| 395 | Ag20Ta4 | Ag5Ta-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.84661 |\n", + "| 396 | Ag20Ta4 | Ag5Ta-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.715044 |\n", + "| 397 | Ag20Ta4 | Ag5Ta-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.03712 |\n", + "| 398 | Cu20Nb4 | Cu5Nb-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.31073 |\n", + "| 399 | Cu20Nb4 | Cu5Nb-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.77539 |\n", + "| 400 | Cu20Nb4 | Cu5Nb-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.5755 |\n", + "| 401 | Au20Hf4 | Au5Hf-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.18353 |\n", + "| 402 | Au20Hf4 | Au5Hf-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.196989 |\n", + "| 403 | Au20Hf4 | Au5Hf-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.35449 |\n", + "| 404 | Au20Hf4 | Au5Hf-fcc-SPIN2 | 111 | Li(g) + * -> Li* | -1.51912 |\n", + "| 405 | Ag20Zr4 | Ag5Zr-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.05845 |\n", + "| 406 | Ag20Zr4 | Ag5Zr-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.416477 |\n", + "| 407 | Ag20Zr4 | Ag5Zr-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.4389 |\n", + "| 408 | Au20Ti4 | Au5Ti-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.29748 |\n", + "| 409 | Au20Ti4 | Au5Ti-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.276835 |\n", + "| 410 | Au20Ti4 | Au5Ti-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.38071 |\n", + "| 411 | Au9Pt71 | Au9Pt71-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.455912 |\n", + "| 412 | Ag20Co4 | Ag5Co-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.22235 |\n", + "| 413 | Ag20Co4 | Ag5Co-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.19117 |\n", + "| 414 | Ag20Co4 | Ag5Co-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.32752 |\n", + "| 415 | Cu24 | Cu6-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.61749 |\n", + "| 416 | Cu24 | Cu6-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.3437 |\n", + "| 417 | Cu24 | Cu6-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.8062 |\n", + "| 418 | Ag20Fe4 | Ag5Fe-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.24456 |\n", + "| 419 | Ag20Fe4 | Ag5Fe-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0847466 |\n", + "| 420 | Ag20Fe4 | Ag5Fe-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.46823 |\n", + "| 421 | Cu20Ta4 | Cu5Ta-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.3741 |\n", + "| 422 | Cu20Ta4 | Cu5Ta-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.77837 |\n", + "| 423 | Cu20Ta4 | Cu5Ta-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.60271 |\n", + "| 424 | Cu20Fe4 | Cu5Fe-fcc-SPIN1 | 111 | 0.5N2(g) + * -> N* | 1.42172 |\n", + "| 425 | Cu20Fe4 | Cu5Fe-fcc-SPIN1 | 111 | 0.5O2(g) + * -> O* | -1.66299 |\n", + "| 426 | Cu20Fe4 | Cu5Fe-fcc-SPIN1 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.55508 |\n", + "| 427 | Au71Ir9 | Au71Ir9-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | 0.72906 |\n", + "| 428 | Ag20Ni4 | Ag5Ni-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 3.28689 |\n", + "| 429 | Ag20Ni4 | Ag5Ni-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.117533 |\n", + "| 430 | Ag20Ni4 | Ag5Ni-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 4.28935 |\n", + "| 431 | Pt20V4 | Pt5V-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 2.29058 |\n", + "| 432 | Pt20V4 | Pt5V-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.122412 |\n", + "| 433 | Pt20V4 | Pt5V-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.14563 |\n", + "| 434 | Cu20Hf4 | Cu5Hf-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.38616 |\n", + "| 435 | Cu20Hf4 | Cu5Hf-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.6714 |\n", + "| 436 | Cu20Hf4 | Cu5Hf-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 2.92572 |\n", + "| 437 | Au55Ir9Re16 | Au55Ir9Re16-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -0.0541987 |\n", + "| 438 | Cu20Ti4 | Cu5Ti-fcc-SPIN2 | 111 | 0.5N2(g) + * -> N* | 1.52941 |\n", + "| 439 | Cu20Ti4 | Cu5Ti-fcc-SPIN2 | 111 | 0.5O2(g) + * -> O* | -1.51281 |\n", + "| 440 | Cu20Ti4 | Cu5Ti-fcc-SPIN2 | 111 | CH4(g) - 1.5H2(g) + * -> CH* | 3.1031 |\n" + ] + } + ], + "source": [ + "import pandas\n", + "pub_id = \"SainiElectronic2022\"\n", + "dataframe = pandas.read_pickle(pub_id + '.pickle')\n", + "\n", + "print(dataframe.columns)\n", + "print(dataframe[['chemical_composition', 'surface_composition','facet', 'equation', 'reaction_energy']].to_markdown())" + ] + }, + { + "cell_type": "markdown", + "id": "3fc80015-f7ea-4b8c-9f69-31d4551c9504", + "metadata": {}, + "source": [ + "## Atomic structures\n", + "\n", + "If you selected `include_atoms=True` in the query above, the atomic structures are also stored in the dataframe (as ASE Atoms objects). And the structures for a spefific reaction can be selected like this:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "86338d19-fbc8-4a3a-9596-aaf988fa40e2", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'atoms'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32mpandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'atoms'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mase\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvisualize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m view\n\u001b[1;32m 2\u001b[0m row_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m\n\u001b[0;32m----> 3\u001b[0m atoms_list \u001b[38;5;241m=\u001b[39m \u001b[43mdataframe\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43matoms\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m[row_id]\n\u001b[1;32m 4\u001b[0m atoms_name \u001b[38;5;241m=\u001b[39m dataframe[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124matoms_name\u001b[39m\u001b[38;5;124m'\u001b[39m][row_id]\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mprint\u001b[39m(atoms_name)\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3504\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3507\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/.pyenv/versions/3.9.7/lib/python3.9/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3621\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3623\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3625\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3626\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3627\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3628\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'atoms'" + ] + } + ], + "source": [ + "from ase.visualize import view\n", + "row_id = 4\n", + "atoms_list = dataframe['atoms'][row_id]\n", + "atoms_name = dataframe['atoms_name'][row_id]\n", + "print(atoms_name)\n", + "idx = 2\n", + "\n", + "view(atoms_list[idx], viewer='ngl')" + ] + }, + { + "cell_type": "markdown", + "id": "287392c3-5fff-489b-84ef-4e0c4e3348ee", + "metadata": {}, + "source": [ + "If you selected `include_atoms=False` to save time on the query, it's still possible to query the atomic objects by using the `get_atoms_for_reaction()` function to only fetch structures for a few reactions. For this we need to use the the reaction_id entry in the dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "905410cf-840a-4db5-bb27-3c481f20d71a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Querying database\n", + "['star', 'N2gas', 'Nstar']\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "06c616d8a68b4a96b6bd6366d22a3e7b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(NGLWidget(), VBox(children=(Dropdown(description='Show', options=('All', 'Fe', 'N', 'Pt'), valu…" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ase.visualize import view\n", + "row_id = 4\n", + "reaction_id = dataframe['reaction_id'][row_id]\n", + "atoms_list = db.get_atoms_for_reaction(reaction_id)\n", + "atoms_name = dataframe['atoms_name'][row_id]\n", + "print(atoms_name)\n", + "idx = 2\n", + "\n", + "view(atoms_list[idx], viewer='ngl')" + ] + }, + { + "cell_type": "markdown", + "id": "3671f9e0-2359-4e94-8ac4-16296d63e764", + "metadata": {}, + "source": [ + "### Filtering the data\n", + "\n", + "Data can be filtered using pandas syntax, for example selecting a specific chemical reaction like this: \n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d0862c54-743d-4eef-a8ff-dc85414ab97b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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Mtt56ax577DEef/zx5e+/9957ufvuu3ts55lnnuHiiy9mp5126vaYNdZYg+OOO46JEyfyyU9+sl++v2a1fQ5XZj4PbNzudiVJUvv96le/YtGiRXzjG99ouLTDpz/9aT772c8yd+5cDjroII477jimTJnC9OnTWbhwISeeeOJK62Sdcsop3H333eyxxx5svvnmPPDAA5xyyik8+uijnH322aus5zOf+Uyv5oz1lY/2kSRpkBiMNyjMnDmT0aNHc9BBBzXcf/DBBzN9+nRmzpzJGWecwfnnn8+xxx7LpEmT2HbbbfnmN7+50iN5tttuOy644AIuuOACnn76adZbbz123XVXfvSjH62yh6uTDFySJKmYZcswdGf99dfnhRdeWP71pEmTmDRp0grH7LPPPit8PXHiRCZOnNhU+/WP82lk/vz5TZ2rL3yWoiRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCfLSPJEmDxdUndrb9PY7p8yluuOEGZsyYwfXXX8/ChQsZPXo0O+64I1OmTGHKlCkMGzas6XONGzeOBx54YKXtF1xwwQqPB1p23MEHH8w555yz0vF77LEHc+fOZdddd+X666/v1ffVEwOXJElqi29961tMnz6dPffck5NOOomtt96aJ598kiuuuILPfvazbLDBBuy///4tnfN973sfxx9//Arbtttuu5WOGz16NBdeeCHPPvsso0ePXr79gQce4JprrllhWwkGLkmSVNy1117L9OnTOeKII/jOd76zwr7999+f6dOn8/zzz7d83k022YSdd965x+P23ntvZs+ezc9+9jOmTZu2fPusWbMYN24cW221Fa+88krL7TfLOVySJKm4k046iY022oiTTz654f5tttmGt771rRx//PFExEr7p02bxrhx43rd/qhRo5g8eTKzZs1aYfusWbM49NBDG7bZnwxckiSpqFdeeYWrr76affbZh5EjR/bruS+66CLWXnttRowYwc4778yFF17Y7bFTp05l7ty5PPjggwDceOON3HPPPUydOrVfa2rEwCVJkopauHAhL774IltvvXW/nnfixImceuqpXH755Zx99tmMHDmSAw44gLPOOqvh8bvvvjtbbbXV8v1nnnkmu+yyC9tss02/1tWIgUuSJA1Kp556KlOnTuU973kPkydP5sorr2T8+PEcc0zjuykjgilTpjBr1iwWL17Meeed15beLTBwSZKkwjbeeGNGjRrVcAmH/jRs2DAOOuggHnzwQR555JGGx0ydOpU777yTE044geeff56PfvSjRWtaxsAlSZKKGj58OBMmTGD27Nm8/PLLqzx22RyvxYsXr7B90aJFLbXZ3ST4bbfdlne+85187WtfY+LEiWywwQYtnbe3DFySJKm4o48+mkWLFvHFL36x4f7777+f22+/ffk8rzvuuGP5vqeeeopf//rXPbaxdOlSzjvvPMaOHctrXvOabo/74he/yMSJEzniiCNa/C56z3W4JElScbvtthvf/OY3mT59OnfeeSfTpk1j7NixPPnkk1x55ZWcfvrpnHPOObz//e9n/fXX57DDDuOEE07g5Zdf5uSTT2bddddd4Xznnnsuv/jFL9hvv/3YaquteOyxx/je977HLbfcwrnnnrvKWg488EAOPPDAkt/uSgxckiQNFv3waJ1OOvLII9lpp52YMWMGX/jCF5Y/2mf8+PH88Ic/ZOLEiayxxhpcfPHFHHXUUXzkIx9hyy235Etf+hJz5sxh7ty5y8/1ute9jgULFvDP//zPPPHEE6yzzjqMHz+eyy67jPe9732d+ya7YeCSJElts8suu7DLLrus8ph3v/vd3HTTTStsmzJlygpf77zzzlx11VVNtTl//vwej+ka5kpwDpckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJGkAysxOl6A6ffmZGLgkSRpg1lxzTV588cVOl6E6L774IiNGjOjVew1ckiQNMJtuuikPPfQQL7zwgj1dHZaZLFmyhCeeeIIHH3yQjTfeuFfnceFTSZIGmPXWWw+Ahx9+mCVLlnS4Gg0fPpyRI0cyduzY5Q/Xbvkc/VyTJEnqB+utt97y4KXBzyFFSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKmwtgeuiNggIs6PiD9GxF0R8a521yBJktROwzvQ5reByzJzckSsBazdgRokSZLapq2BKyLWB3YDpgFk5mJgcTtrkCRJard2Dym+Dngc+O+I+F1EnB4R67S5BkmSpLZqd+AaDuwI/CAz3w48Dxxdf1BEHB4R8yJi3uOPP97mEiVJkvpXuwPXg8CDmfmb2tfnUwWwFWTmaZk5PjPHjxkzpq0FSpIk9be2Bq7MfBT4S0RsV9v0XuDOdtYgSZLUbp24S/EfgbNrdyjeB3yiAzVIkiS1TdsDV2beCoxvd7uSJEmd4krzkiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSChve7gYjYj7wLPAKsDQzx7e7BkmSpHZqe+Cq2SMzF3aobUmSpLZySFGSJKmwTgSuBK6IiJsj4vAOtC9JktRWnRhSfHdmPhQRmwKzI+KPmXlt1wNqQexwgLFjx3agxAHi6hP771x7HNN/5+ovzX5/A7F2ddSM2ff0+r1H7b1tP1Yy8PTl2rRbb2sd6j9DDU1t7+HKzIdqHxcAFwA7NTjmtMwcn5njx4wZ0+4SJUmS+lVbA1dErBMRo5d9DuwD3NHOGiRJktqt3UOKmwEXRMSyts/JzMvaXIMkSVJbtTVwZeZ9wNva2aYkSVKnuSyEJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmHDW31DRLwV2A3YGPhhZj4aEa8HHsvMZ/u7QEmSpMGu6cAVESOAs4ADgQASuAh4FDgZuAc4ukCNkiRJg1orQ4pfAfYCDgU2owpdy1wKvK8f65IkSRoyWhlSPBg4NjPPiYhhdfvuB8b1W1WSJElDSCs9XBsDd63iPCP6Xo4kSdLQ00rguh94Vzf7dgLu7ns5kiRJQ08rgetM4OiIOARYs7YtI2IP4Cjgx/1dnCRJ0lDQSuA6GfgVMAt4srbtemAOcFlmntrPtUmSJA0JTU+az8xXgI9FxPeo7kjcFFhEFbauKVSfJEnSoNfywqeZeR1wXYFaJEmShqSmhxQj4oMRcUQ3+z4XEfv1X1mSJElDRytzuI4D1ulm36jafkmSJNVpJXC9Ebilm323Am/qczWSJElDUCuBaw1g3W72jebVpSIkSZLURSuB6zbgkG72HQLc3vdyJEmShp5W7lL8BvCziPgp8F/Ag8AWwOHAAcBB/V+eJEnS4NfKOlwXRMTnga8AB9Y2B/Ac8E+Z+fMC9UmSJA16La3DlZmnRsQZwC5UD7NeCPw6M59r5TwRMQyYBzyUmR9s5b2SJEmDTW8WPn0WuLyP7X4euAtYr4/nkSRJGvBaClwRsQawEzAWGFm/PzPPbOIcWwIfoBqanN5K+5IkSYNR04ErIrYHLgS2oZq7VS+BHgMX8C3gi1RLSUiSJA15rfRwfb92/EeA3wMvt9pYRHwQWJCZN0fEhFUcdzjV3Y+MHTu21WYkqd/NmH1Pr9531N7b9nMlA09vr420OmklcO0ITOvj3Yi7Ah+qPXdxJLBeRJyVmVO6HpSZpwGnAYwfPz770J4kSVLHtbLw6UJgcV8ay8xjMnPLzBwHfAy4qj5sSZIkDTWtBK4ZwOdqSzpIkiSpSa0MKY4BtgPujIjZwBN1+zMz/63Zk2XmXGBuC+1LkiQNSq0ErmO7fP6GBvsTaDpwSZIkrS5aebRPK8OPkiRJqjFESZIkFdZS4IrKhyLilIj474jYurZ994jYvEyJkiRJg1srK81vCFwCvBN4FlgXOBV4ADiMahL9PxWoUZIkaVBrpYfr68BWVIuXbsyKj/eZA7y3H+uSJEkaMlq5S3F/4AuZeUODtbj+TBXGJEmSVKeVHq51gYe62TeSxg+0liRJWu21ErjuBvbpZt/uVA+0liRJUp1WhhS/D3w3Ip4Gzqlt2yAiPgEcARze38VJkiQNBa0sfHpaRPwNcALw5drm2cBfgZMz8+wC9UmSJA16rfRwkZlHR8QPgL2BTYFFwOzMvK9EcZIkSUNBU4ErItYCHgWmZeYvgdOLViVJkjSENDVpPjMXA0uBl8qWI0mSNPS0cpfihcDkQnVIkiQNWa3M4boU+E5EnE8Vvh4BsusBmXlV/5UmSZI0NLQSuH5W+3hg7bVMUi16mkD9CvSSJEmrvVYC1x7FqpAkSRrCWlmH65qShUiSJA1VrUyalyRJUi803cMVET1NiM/MfG8f65EkSRpyWpnDtQZ1dyUCGwPbAY8D9/RXUZIkSUNJK3O4JjTaHhHbUC0T8dX+KUmSJGlo6fMcrsy8F/ga8PW+lyNJkjT09Nek+ceBbfvpXJIkSUNKnwNXRGwMTAfu7Xs5kiRJQ08rdynez8qT5tcCNqt9/uH+KkqSJGkoaeUuxWtYOXC9BDwA/LQ2l0uSJEl1WrlLcVrBOiRJkoaspudwRcSaEbFON/vWiYg1+68sSZKkoaOVIcUf1Y7/+wb7fggsBj7ZH0VJkiQNJa3cpTgB+EU3+34J+FgfSZKkBloJXJsCC7rZ9ziv3q0oSZKkLloJXAuAt3Sz7y3Aor6XI0mSNPS0ErguBo6LiLd23RgRbwH+FbioPwuTJEkaKlqZNP8lYG/g5oi4CXgQ2ALYCbgfOLb/y5MkSRr8mu7hysyFwDuAE4EAdqh9/Arwjtp+SZIk1Wmlh4vMfIqqp+tLRaqRJEkaglpZ+HTbiNi9m327RcQb+q8sSZKkoaOVSfPfAiZ2s++DwIw+VyNJkjQEtRK4xgPXdrPvWqr5XZIkSarTSuAaDbzUzb4lwPp9L0eSJGnoaSVw3Uf3j+/ZE5jf52okSZKGoFYC15nAURHxuYgYARARIyLic8CRwMwC9UmSJA16rSwLcQrVPK1TgW9HxBPARlSh7WfASf1fniRJ0uDXdODKzFeAyRGxB7APsDGwELgiM+eWKU+SJGnwazpwRcQ7qOZqbVXbdC9wVWbeVKIwSZKkoaLHwBURW1DN35pA9SifrjIirgGmZuaD/V+eJEnS4LfKSfMRsQEwl+q5iUcDbwJG1V5vAo4B3gpcXTtWkiRJdXq6S/FoqvW3dszMr2fm3Zn5cu11d2aeTDWRfnTtWEmSJNXpKXAdAHwtMx/o7oDMvJ/qDsUD+rMwSZKkoaKnwDUWuLmJ89xcO3aVImJkRPw2Im6LiD9ExAnNFClJkjSY9TRp/nmqtbZ6siHwQhPHvQzsmZnPRcSawPURcWlm3tjEeyVJkgalnnq4fgsc2sR5ptaOXaWsPFf7cs3aK5s4vyRJ0qDVUw/Xt4DLIuIU4F8yc3HXnRGxFvBVYBLw/mYajIhhVEOQrwe+l5m/aXDM4cDhAGPH9jhSqf5y9YnNHbfHMf13Lq3WZsy+Z8i32YnvUdLAs8rAlZlXRMSxwL8DUyNiNq8+pHocsDfVivPHZ+YVzTRYW7F+h9oyEhdExJsz8466Y04DTgMYP368PWCSJGlQ63Hh08z8akTcAHyRqidrVG3XS8A1wCmZeWWrDWfmUxFxNbAvcEdPx0uSJA1WTT3aJzOvplrcdBhVjxbAolpvVdMiYgywpBa2RlH1kPnQa0mSNKQ1/SxFWD4cuKAP7b0WmFkLbmsAP8nMi/twPkmSpAGvpcDVV5l5O/D2drYpSZLUaT0tCyFJkqQ+MnBJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQV1tbAFRFbRcTVEXFnRPwhIj7fzvYlSZI6YXib21sK/N/MvCUiRgM3R8TszLyzzXVIkiS1TVt7uDLzkcy8pfb5s8BdwBbtrEGSJKnd2t3DtVxEjAPeDvymwb7DgcMBxo4d297C2uHqEztdQd8M9volSWqzjkyaj4h1gZ8BR2bmM/X7M/O0zByfmePHjBnT/gIlSZL6UdsDV0SsSRW2zs7Mn7e7fUmSpHZr912KAfwIuCszv9nOtiVJkjql3T1cuwKHAntGxK21135trkGSJKmt2jppPjOvB6KdbUqSJHWaK81LkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqrK2BKyJ+HBELIuKOdrYrSZLUSe3u4ToD2LfNbUqSJHVUWwNXZl4LPNHONiVJkjrNOVySJEmFDe90AY1ExOHA4QBjx44t3+DVJ/Z8zB7H9N+5OmGg1tWM/qy92Z/jYNafv8/SADRj9j2dLqFpR+29ba/e19vvsbftDSaD9doMyB6uzDwtM8dn5vgxY8Z0uhxJkqQ+GZCBS5IkaShp97IQ5wI3ANtFxIMR8al2ti9JktQJbZ3DlZkHt7M9SZKkgcAhRUmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYUZuCRJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySJEmFGbgkSZIKM3BJkiQVZuCSJEkqzMAlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFWbgkiRJKszAJUmSVJiBS5IkqTADlyRJUmEGLkmSpMIMXJIkSYW1PXBFxL4RcXdE/Ckijm53+5IkSe3W1sAVEcOA7wHvB7YHDo6I7dtZgyRJUru1u4drJ+BPmXlfZi4G/h+wf5trkCRJaqt2B64tgL90+frB2jZJkqQhKzKzfY1FTAb2zcx/qH19KPDOzDyi7rjDgcNrX24H3N22Il+1CbCwA+0OdF6XxrwuK/OaNOZ1aczrsjKvSWMD/bpsnZlj6jcOb3MRDwFbdfl6y9q2FWTmacBp7SqqkYiYl5njO1nDQOR1aczrsjKvSWNel8a8LivzmjQ2WK9Lu4cUbwLeEBGvi4i1gI8Bv2xzDZIkSW3V1h6uzFwaEUcAlwPDgB9n5h/aWYMkSVK7tXtIkcy8BLik3e32QkeHNAcwr0tjXpeVeU0a87o05nVZmdeksUF5Xdo6aV6SJGl15KN9JEmSCjNw1UTE1yPijxFxe0RcEBEbdHPcavVooog4KCL+EBF/jYhu7wqJiPkR8fuIuDUi5rWzxk5o4bqsNr8vEbFRRMyOiP+tfdywm+Neqf2e3BoRQ/ammZ5+9hExIiLOq+3/TUSM60CZbdXENZkWEY93+f34h07U2U4R8eOIWBARd3SzPyLiO7VrdntE7NjuGjuhiesyISKe7vK78qV219gqA9erZgNvzsy3AvcAx9QfsJo+mugO4EDg2iaO3SMzdxiMt+v2Qo/XZTX8fTkauDIz3wBcWfu6kRdrvyc7ZOaH2lde+zT5s/8U8GRmvh6YAZzU3irbq4X/Hs7r8vtxeluL7IwzgH1Xsf/9wBtqr8OBH7ShpoHgDFZ9XQCu6/K78uU21NQnBq6azLwiM5fWvryRao2weqvdo4ky867M7MTCswNak9dldft92R+YWft8JjCpc6V0XDM/+67X63zgvRERbayx3Va3/x6akpnXAk+s4pD9gTOzciOwQUS8tj3VdU4T12XQMXA19kng0gbbfTRR9xK4IiJurj0pQKvf78tmmflI7fNHgc26OW5kRMyLiBsjYlJ7Smu7Zn72y4+p/WPvaWDjtlTXGc3+9/Dh2tDZ+RGxVYP9q5vV7e9IK94VEbdFxKUR8bedLqYnbV8WopMiYg7wmga7/jUzf1E75l+BpcDZ7aytk5q5Lk14d2Y+FBGbArMj4o+1f6EMWv10XYaUVV2Trl9kZkZEd7dAb137Xfkb4KqI+H1m3tvftWpQugg4NzNfjohPU/UA7tnhmjQw3UL1t+S5iNgPuJBq2HXAWq0CV2butar9ETEN+CDw3my8XkZTjyYabHq6Lk2e46HaxwURcQHV8MGgDlz9cF2G3O/Lqq5JRDwWEa/NzEdqQx4LujnHst+V+yJiLvB2YKgFrmZ+9suOeTAihgPrA4vaU15H9HhNMrPr9386cHIb6hrohtzfkf6Qmc90+fySiPh+RGySmQP2GYsOKdZExL7AF4EPZeYL3Rzmo4kaiIh1ImL0ss+Bfagmla/uVrffl18CH699/nFgpV7AiNgwIkbUPt8E2BW4s20Vtk8zP/uu12sycFU3/9AbKnq8JnVzkz4E3NXG+gaqXwJTa3cr7gw83WXofrUVEa9ZNucxInaiyjMD+x8smemr+hv3J6px8ltrr/+sbd8cuKTLcftR3cV4L9XQUsdrL3xdDqCaM/Ay8Bhwef11Af4GuK32+oPXZfX8faGaf3Ql8L/AHGCj2vbxwOm1z3cBfl/7Xfk98KlO113weqz0swe+TPWPOoCRwE9rf3t+C/xNp2seANfkxNrfkNuAq4E3drrmNlyTc4FHgCW1vymfAj4DfKa2P6ju7ry39t/M+E7XPECuyxFdflduBHbpdM09vVxpXpIkqTCHFCVJkgozcEmSJBVm4JIkSSrMwCVJklSYgUuSJKkwA5ckSVJhBi5JkqTCDFySBpyI+K+IyIiY0WDftNq+Rq+nWmjjOxFxcYt1XRgRTy5bLb/B/tER8XxEnFH7+siI+H1E+LdWWs35R0DSgBIRo4CPAAn8fe05g40cBLyr7tXU8y8jYhuqVauPb7G8mcAGVM9cbWQysHbtOIAfAmN49RE+klZTBi5JA80kYD2qBxdvCuzbzXG3ZuaNda95TbZxJHBbC8cv8yuq57VN7Wb/VODPwFyAzHwROBP4QovtSBpiDFySBpqPA/cDxwGP08+9Q7XhwCnAOQ32vS0iflkbNnwxIv4nIt6zbH9mLqZ6xtv7I2LjuveOBXYHZuWKz0z7f8D2EbFLf34fkgYXA5ekASMiNqcaFjwrM5dQhZWJEbFhg8OHRcTwulczf9N2phoWvK6u7R2BXwMbAYcBH6bqzZoTEX/X5dCZwJrAx+rOO4XqQcNn1m2/FXiW7nvqJK0GDFySBpIpwDDgrNrXZwIjgI82OPaPwJK61y+baGNnqvlht9dt/zrVcOCemXl+Zl4CHADcR9XbBkBtGPJOVh5WPBS4MTPv6boxM/8K3FZrV9JqysAlaSD5OPCbZaGlFm7+SONhxQOAd9S9jmyijc2BZ2rDg8Dyifq7Az8F/rqsx4yqx2oOsFvdOWYCO0XEtrX37wS8kVcny9d7vNaupNWUgUvSgBAR44HtgVl1u2YBOy8LN13ckZnz6l5/aqKpkcDLdds2oupZO46Ve82OADasG648C/grr/ZyTa2d87xu2nwRGNVEbZKGKAOXpIHi41QBpz60nEU1BNjdnYGtWkQ1h6urp6gC1Kms3Gv2DuAdtaFBADLzYWA2MCUi1qIa8rwoM5/sps2NgIX9VL+kQai79W0kqW1qoeVg4NLMXCGYZOafI+Ia4NCIOK7hCVrzR2CtiNgyMx+stfF8RFwHvA24pWu4WoWZVHc6nghsQvfDiQCvA37bt7IlDWYGLkkDwQeAjYG/RMSkBvvvAybUXsvsEBGbNDh2XmYuXUVb19Y+7gQ82GX79Nq+yyPiR8AjVEFqR2BYZh5dd54LgWeAo4AFwGWNGouIDYBtgVNWUZOkIc7AJWkgWDYp/nO116qOm1v7/KfdHDOGVQzfZeb8iPgtMBH4eZftt0TEO4B/A74DrE812f0W4D8bnOfFiPgJ8A/AOasIeR8AFgMXdP9tSRrqYsX1+SRp6IuIacC3gddm5guF27oUWJiZh5ZsR9LAZuCStNqpLfnwe+BHmVlsqC8idgB+A/xtk3dQShqivEtR0mqnNvz3CaBo7xbwGmCaYUuSPVySJEmF2cMlSZJUmIFLkiSpMAOXJElSYQYuSZKkwgxckiRJhRm4JEmSCjNwSZIkFfb/AXtf96MPLXa5AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "equation = '0.5O2(g) + * -> O*' \n", + "\n", + "loc1 = (dataframe[\"equation\"] == equation) & (dataframe['surface_composition'].str.contains('Au5'))\n", + "loc2 = (dataframe[\"equation\"] == equation) & (dataframe['surface_composition'].str.contains('Cu5'))\n", + "dataframe_1 = dataframe[loc1]\n", + "dataframe_2 = dataframe[loc2]\n", + "\n", + "import pylab as p\n", + "p.figure(figsize=(10,8))\n", + "p.hist(dataframe_1['reaction_energy'].values, bins=20, alpha=0.5, label='Au5M')\n", + "p.hist(dataframe_2['reaction_energy'].values, bins=20, alpha=0.5, label='Cu5M')\n", + "p.title('0.5O2(g) + * -> O*', fontsize=18)\n", + "p.ylabel('Occurence', fontsize=16)\n", + "p.xlabel('$\\Delta$E (eV)', fontsize=16)\n", + "p.legend(prop={'size':16})\n", + "p.show()" + ] + }, + { + "cell_type": "markdown", + "id": "7d2119d6-a118-4a52-ab91-ad9b6033125f", + "metadata": {}, + "source": [ + "### Challenge: Refine query based on chemical composition, adsorbates and facet\n", + "It is also posible to filter the data already on the level of the cathub query, and to search across publications" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b60284b0-3e69-410a-82f6-84ea12636785", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Querying database\n", + "\n", + "| | pub_id | surface_composition | reaction_energy | dft_functional |\n", + "|---:|:------------------------------|:----------------------|------------------:|:-------------------|\n", + "| 0 | TangFrom2020 | Cu-fcc | -0.649572 | BEEF-vdW |\n", + "| 1 | PengRole2020 | Cu-fcc | -0.649572 | BEEF-vdW |\n", + "| 2 | PengRole2020 | Cu-fcc | -0.495651 | BEEF-vdW |\n", + "| 3 | PengRole2020 | Cu-fcc | -0.595639 | BEEF-vdW |\n", + "| 4 | Gauthierrole2021 | Cu | 0.635223 | RPBE_-0.413VSHE |\n", + "| 5 | Gauthierrole2021 | Cu | -0.722366 | RPBE_-0.413VSHE |\n", + "| 6 | Gauthierrole2021 | Cu | 0.131535 | RPBE_-0.413VSHE |\n", + "| 7 | PengTrends2022 | Cu-fcc | -0.649572 | BEEF-vdW |\n", + "| 8 | LiScreening2021 | Cu | -0.630358 | BEEF |\n", + "| 9 | PasumarthiFacetDependence2023 | Cu | -0.640125 | BEEF-vdW_-0.54VSHE |\n", + "| 10 | PasumarthiFacetDependence2023 | Cu | -0.476198 | BEEF-vdW_-0.54VSHE |\n", + "| 11 | PasumarthiFacetDependence2023 | Cu | -0.590538 | BEEF-vdW_-0.54VSHE |\n", + "| 12 | PasumarthiFacetDependence2023 | Cu | -0.65266 | BEEF-vdW_-0.85VSHE |\n", + "| 13 | PasumarthiFacetDependence2023 | Cu | -0.516654 | BEEF-vdW_-0.85VSHE |\n", + "| 14 | PasumarthiFacetDependence2023 | Cu | -0.623223 | BEEF-vdW_-0.85VSHE |\n", + "| 15 | PasumarthiFacetDependence2023 | Cu | -0.641782 | BEEF-vdW_-0.07VSHE |\n", + "| 16 | PasumarthiFacetDependence2023 | Cu | -0.42605 | BEEF-vdW_-0.07VSHE |\n", + "| 17 | PasumarthiFacetDependence2023 | Cu | -0.559446 | BEEF-vdW_-0.07VSHE |\n", + "| 18 | PasumarthiFacetDependence2023 | Cu | -0.63665 | BEEF-vdW_-0.38VSHE |\n", + "| 19 | PasumarthiFacetDependence2023 | Cu | -0.457884 | BEEF-vdW_-0.38VSHE |\n", + "| 20 | PasumarthiFacetDependence2023 | Cu | -0.577809 | BEEF-vdW_-0.38VSHE |\n", + "| 21 | PasumarthiFacetDependence2023 | Cu | -0.637381 | BEEF-vdW_-0.23VSHE |\n", + "| 22 | PasumarthiFacetDependence2023 | Cu | -0.44104 | BEEF-vdW_-0.23VSHE |\n", + "| 23 | PasumarthiFacetDependence2023 | Cu | -0.567257 | BEEF-vdW_-0.23VSHE |\n", + "| 24 | PasumarthiFacetDependence2023 | Cu | -0.644185 | BEEF-vdW_-0.70VSHE |\n", + "| 25 | PasumarthiFacetDependence2023 | Cu | -0.497065 | BEEF-vdW_-0.70VSHE |\n", + "| 26 | PasumarthiFacetDependence2023 | Cu | -0.606393 | BEEF-vdW_-0.70VSHE |\n", + "| 27 | PasumarthiFacetDependence2023 | Cu | -0.664365 | BEEF-vdW_-1.01VSHE |\n", + "| 28 | PasumarthiFacetDependence2023 | Cu | -0.539687 | BEEF-vdW_-1.01VSHE |\n", + "| 29 | PasumarthiFacetDependence2023 | Cu | -0.643348 | BEEF-vdW_-1.01VSHE |\n", + "| 30 | PasumarthiDoubleLayer2023 | Cu | -0.640125 | BEEF-vdW |\n", + "| 31 | PasumarthiDoubleLayer2023 | Cu | -0.476198 | BEEF-vdW |\n", + "| 32 | PasumarthiDoubleLayer2023 | Cu | -0.590538 | BEEF-vdW |\n", + "| 33 | PasumarthiDoubleLayer2023 | Cu | -0.586962 | BEEF-vdW_1MLH2O |\n", + "| 34 | PasumarthiDoubleLayer2023 | Cu | -0.559777 | BEEF-vdW_1MLH2O |\n", + "| 35 | PasumarthiDoubleLayer2023 | Cu | -0.521223 | BEEF-vdW_1MLH2O |\n" + ] + } + ], + "source": [ + "dataframe2 = db.get_dataframe(reactants={'COgas': 1},\n", + " products={'COstar': 1},\n", + " #elements=['Cu', 'Al'], #contains Cu and Al\n", + " surface_composition='Cu', # match specific composition\n", + " facet = '100'\n", + " )\n", + "print(dataframe2[['pub_id', 'surface_composition', 'reaction_energy', 'dft_functional']].to_markdown())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/ACS_fall_2023/2_scaling.ipynb b/tutorials/ACS_fall_2023/2_scaling.ipynb new file mode 100644 index 0000000..3e8a22a --- /dev/null +++ b/tutorials/ACS_fall_2023/2_scaling.ipynb @@ -0,0 +1,698 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0d926953-dc8c-434e-9ac2-3d96715012a9", + "metadata": {}, + "source": [ + "# Demo 2: Scaling relations and volcano plots for metal oxides\n", + "In this demonstration you will learn how to plot scaling relations and volcano plots in python. We will use the O and OH adsorption energies from our 2022 transition metal oxides dataset https://www.catalysis-hub.org/publications/ComerUnraveling2022 (publication link: https://pubs.acs.org/doi/10.1021/acs.jpcc.2c02381)\n", + "\n", + "Start by connecting to the remote server and fetching the pandas dataframe (as covered in previous first demonstration):\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a55382e8-d325-46c9-ab86-9e02459bae63", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from cathub.cathubsql import CathubSQL\n", + "db = CathubSQL()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "eb9d0678-221d-491a-8cfc-f20a569cbc11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Querying database\n", + "\n", + "Data downloaded!\n" + ] + } + ], + "source": [ + "dataframe = db.get_dataframe(pub_id='ComerUnraveling2022')\n", + "dataframe.to_pickle('ComerUnraveling2022.pickle')\n", + "print('Data downloaded!')" + ] + }, + { + "cell_type": "markdown", + "id": "b624cbd6-7557-4168-917d-7ce8d6f3367c", + "metadata": {}, + "source": [ + "Now you can read the data from a local pickle file:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cbf3c826-2bc4-41f1-b6f8-d532333ef672", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas\n", + "dataframe = pandas.read_pickle('ComerUnraveling2022.pickle')" + ] + }, + { + "cell_type": "markdown", + "id": "3d56fda5-02ef-43c8-943f-3ea0eaf6920f", + "metadata": {}, + "source": [ + "Start by examining the unique chemical reactions and facets for the dataset, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17275f1a-2e0b-4309-8def-c70b2bb43644", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| | reaction_id | surface_composition | facet | equation | reaction_energy |\n", + "|----:|--------------:|:----------------------|--------:|:-----------------------------|------------------:|\n", + "| 0 | 436020 | FeO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.7116 |\n", + "| 1 | 436021 | FeO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.88222 |\n", + "| 2 | 436022 | FeO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.526 |\n", + "| 3 | 436023 | FeO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.83325 |\n", + "| 4 | 436024 | MnO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.09478 |\n", + "| 5 | 436025 | MnO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.5347 |\n", + "| 6 | 436026 | MnO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.01555 |\n", + "| 7 | 436027 | MnO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.47976 |\n", + "| 8 | 436028 | VO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 1.9588 |\n", + "| 9 | 436029 | VO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 0.193726 |\n", + "| 10 | 436030 | VO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 2.18619 |\n", + "| 11 | 436031 | VO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 0.70303 |\n", + "| 12 | 436032 | AuO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.10672 |\n", + "| 13 | 436033 | AuO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.50575 |\n", + "| 14 | 436034 | AuO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.08981 |\n", + "| 15 | 436035 | AuO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.39273 |\n", + "| 16 | 436036 | InO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.85162 |\n", + "| 17 | 436037 | InO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.12769 |\n", + "| 18 | 436038 | InO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.6095 |\n", + "| 19 | 436039 | InO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.02766 |\n", + "| 20 | 436040 | TcO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 0.541237 |\n", + "| 21 | 436041 | TcO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -0.363555 |\n", + "| 22 | 436042 | TcO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 0.926029 |\n", + "| 23 | 436043 | TcO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 0.167335 |\n", + "| 24 | 436044 | ZrO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.64453 |\n", + "| 25 | 436045 | ZrO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.71593 |\n", + "| 26 | 436046 | ZrO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.57736 |\n", + "| 27 | 436047 | ZrO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.77081 |\n", + "| 28 | 436048 | OsO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 0.390741 |\n", + "| 29 | 436049 | OsO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -0.659404 |\n", + "| 30 | 436050 | OsO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 0.636795 |\n", + "| 31 | 436051 | OsO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -0.363018 |\n", + "| 32 | 436052 | HfO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.47125 |\n", + "| 33 | 436053 | HfO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.53663 |\n", + "| 34 | 436054 | HfO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.42943 |\n", + "| 35 | 436055 | HfO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.5856 |\n", + "| 36 | 436056 | NiO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.48088 |\n", + "| 37 | 436057 | NiO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.09728 |\n", + "| 38 | 436058 | NiO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.0942 |\n", + "| 39 | 436059 | NiO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.60354 |\n", + "| 40 | 436060 | MoO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | -0.276344 |\n", + "| 41 | 436061 | MoO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -0.672943 |\n", + "| 42 | 436062 | MoO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | -0.055886 |\n", + "| 43 | 436063 | MoO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -0.624382 |\n", + "| 44 | 436064 | ReO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | -0.497 |\n", + "| 45 | 436065 | ReO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -1.24567 |\n", + "| 46 | 436066 | ReO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 0.0921325 |\n", + "| 47 | 436067 | ReO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -0.596807 |\n", + "| 48 | 436068 | PbO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 5.10974 |\n", + "| 49 | 436069 | PbO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.004 |\n", + "| 50 | 436070 | PbO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.81053 |\n", + "| 51 | 436071 | PbO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.9784 |\n", + "| 52 | 436072 | NbO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | -0.247106 |\n", + "| 53 | 436073 | NbO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -1.03315 |\n", + "| 54 | 436074 | NbO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 0.230584 |\n", + "| 55 | 436075 | NbO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -0.7488 |\n", + "| 56 | 436076 | CuO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.75157 |\n", + "| 57 | 436077 | CuO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.28846 |\n", + "| 58 | 436078 | CuO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.70905 |\n", + "| 59 | 436079 | CuO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.2781 |\n", + "| 60 | 436080 | GeO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.40734 |\n", + "| 61 | 436081 | GeO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.60255 |\n", + "| 62 | 436082 | GeO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.50571 |\n", + "| 63 | 436083 | GeO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.78437 |\n", + "| 64 | 436084 | CdO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 5.02465 |\n", + "| 65 | 436085 | CdO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.25677 |\n", + "| 66 | 436086 | CdO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.70841 |\n", + "| 67 | 436087 | CdO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.08067 |\n", + "| 68 | 436088 | CrO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 3.23404 |\n", + "| 69 | 436089 | CrO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.12565 |\n", + "| 70 | 436090 | CrO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 3.40305 |\n", + "| 71 | 436091 | CrO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.3257 |\n", + "| 72 | 436092 | RuO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 1.62545 |\n", + "| 73 | 436093 | RuO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 0.168 |\n", + "| 74 | 436094 | RuO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 1.82744 |\n", + "| 75 | 436095 | RuO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 0.412744 |\n", + "| 76 | 436096 | ZnO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.81723 |\n", + "| 77 | 436097 | ZnO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.23053 |\n", + "| 78 | 436098 | ZnO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.75716 |\n", + "| 79 | 436099 | ZnO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.57342 |\n", + "| 80 | 436100 | CoO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 3.77301 |\n", + "| 81 | 436101 | CoO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.38496 |\n", + "| 82 | 436102 | CoO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 3.69092 |\n", + "| 83 | 436103 | CoO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.34296 |\n", + "| 84 | 436104 | RhO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 2.54177 |\n", + "| 85 | 436105 | RhO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 0.462479 |\n", + "| 86 | 436106 | RhO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 2.69526 |\n", + "| 87 | 436107 | RhO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 0.618568 |\n", + "| 88 | 436108 | IrO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 1.46179 |\n", + "| 89 | 436109 | IrO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -0.344821 |\n", + "| 90 | 436110 | IrO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 1.53642 |\n", + "| 91 | 436111 | IrO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -0.241255 |\n", + "| 92 | 436112 | SnO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.43379 |\n", + "| 93 | 436113 | SnO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.40022 |\n", + "| 94 | 436114 | SnO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.52608 |\n", + "| 95 | 436115 | SnO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.66063 |\n", + "| 96 | 436116 | PdO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.11614 |\n", + "| 97 | 436117 | PdO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 0.881481 |\n", + "| 98 | 436118 | PdO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 3.52041 |\n", + "| 99 | 436119 | PdO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.23628 |\n", + "| 100 | 436120 | HgO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 5.44796 |\n", + "| 101 | 436121 | HgO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.80976 |\n", + "| 102 | 436122 | HgO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 5.98221 |\n", + "| 103 | 436123 | HgO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.73882 |\n", + "| 104 | 436124 | TaO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | -1.07164 |\n", + "| 105 | 436125 | TaO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -1.84571 |\n", + "| 106 | 436126 | TaO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | -0.386078 |\n", + "| 107 | 436127 | TaO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -1.46489 |\n", + "| 108 | 436128 | PtO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 2.72525 |\n", + "| 109 | 436129 | PtO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 0.504258 |\n", + "| 110 | 436130 | PtO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 2.59434 |\n", + "| 111 | 436131 | PtO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 0.409204 |\n", + "| 112 | 436132 | WO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | -0.752694 |\n", + "| 113 | 436133 | WO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | -1.45369 |\n", + "| 114 | 436134 | WO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | -0.0273224 |\n", + "| 115 | 436135 | WO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | -0.822337 |\n", + "| 116 | 436136 | TiO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.54936 |\n", + "| 117 | 436137 | TiO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 1.7209 |\n", + "| 118 | 436138 | TiO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.50349 |\n", + "| 119 | 436139 | TiO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.82797 |\n", + "| 120 | 436140 | GaO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.74178 |\n", + "| 121 | 436141 | GaO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.08196 |\n", + "| 122 | 436142 | GaO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.65424 |\n", + "| 123 | 436143 | GaO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.16846 |\n", + "| 124 | 436144 | AgO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 4.77672 |\n", + "| 125 | 436145 | AgO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.0888 |\n", + "| 126 | 436146 | AgO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 4.59639 |\n", + "| 127 | 436147 | AgO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 1.90782 |\n", + "| 128 | 436148 | TlO2-rutile | 110 | H2O(g) - H2(g) + * -> O* | 5.08294 |\n", + "| 129 | 436149 | TlO2-rutile | 110 | H2O(g) - 0.5H2(g) + * -> HO* | 2.40984 |\n", + "| 130 | 436150 | TlO2-rutile | 100 | H2O(g) - H2(g) + * -> O* | 5.47393 |\n", + "| 131 | 436151 | TlO2-rutile | 100 | H2O(g) - 0.5H2(g) + * -> HO* | 2.08295 |\n" + ] + } + ], + "source": [ + "print(dataframe[['reaction_id', 'surface_composition', 'facet', 'equation', 'reaction_energy']].to_markdown())" + ] + }, + { + "cell_type": "markdown", + "id": "5a417974-094a-4780-9a0a-cf182b0ad0e3", + "metadata": {}, + "source": [ + "## Atomic structures\n", + "\n", + "Visualize the structures for the 110 and 100 facets using the ``get_atoms_for_reaction()`` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "39e75240-0bf8-4d0c-a387-f391bd82e894", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Structure names 110 Fe: ['Ostar', 'H2Ogas', 'star', 'H2gas', 'bulkrutile']\n", + "Structure names 100 Fe: ['H2Ogas', 'Ostar', 'H2gas', 'star', 'bulkrutile']\n", + "Querying database\n", + "Querying database\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0336b1a4476d450b9f1438dc2aedaa93", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ef87712490c9446aa83b15a56e51fbb9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(NGLWidget(), VBox(children=(Dropdown(description='Show', options=('All', 'O', 'Fe'), value='All…" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from ase.visualize import view\n", + "print('Structure names 110 Fe:', dataframe['atoms_name'][0])\n", + "print('Structure names 100 Fe:', dataframe['atoms_name'][2])\n", + "atoms_110_Fe = db.get_atoms_for_reaction(436020)\n", + "atoms_100_Fe = db.get_atoms_for_reaction(436022)\n", + "view(atoms_110_Fe[0], viewer='ngl')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "27f9f156-b3a4-4c7d-94f3-2fb636ac9dcd", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6f90b04fcbe54355a9da3104b88689b0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(NGLWidget(), VBox(children=(Dropdown(description='Show', options=('All', 'O', 'Fe'), value='All…" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "view(atoms_100_Fe[1], viewer='ngl')" + ] + }, + { + "cell_type": "markdown", + "id": "47761fff-367a-47fe-bfb3-e49a3ff0496b", + "metadata": {}, + "source": [ + "## Scaling relations\n", + "Use your favorite python plotting module to plot the OH vs. O scaling (i.e. plotting OH vs O adsorption energies).\n", + "\n", + "Example: using matplotlib/pylab you can plot the scaling relation like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "cfebdca5-2c52-4772-9b7f-fd442dc9c881", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import pylab as p\n", + "\n", + "loc_O = (dataframe[\"equation\"] =='H2O(g) - H2(g) + * -> O*') \n", + "loc_OH = (dataframe[\"equation\"] =='H2O(g) - 0.5H2(g) + * -> HO*') \n", + "\n", + "ads_O = dataframe[loc_O]\n", + "ads_OH = dataframe[loc_OH]\n", + "\n", + "# Combine O and OH in one dataframe, matching on surface compositions\n", + "dataframe_together = ads_O.merge(ads_OH, \n", + " on=['surface_composition', 'facet'],\n", + " suffixes=('_O', '_OH'))\n", + "\n", + "x_data = dataframe_together['reaction_energy_OH']\n", + "y_data = dataframe_together['reaction_energy_O']\n", + "p.figure(figsize=(10,10))\n", + "p.scatter(x_data, y_data)\n", + "for i, txt in enumerate(dataframe_together['surface_composition']):\n", + " p.gca().annotate(txt,\n", + " (x_data[i],\n", + " y_data[i]))\n", + "p.title('O-OH Scaling relation', fontsize=20)\n", + "p.xlabel('$\\Delta$E$_{OH}$ (eV)', fontsize=16)\n", + "p.ylabel('$\\Delta$E$_{O}$ (eV)', fontsize=16)\n", + "\n", + "\n", + "fit = np.polyfit(x_data,\n", + " y_data,\n", + " 1)\n", + "\n", + "a = fit[0]\n", + "b = fit[1]\n", + "xfit = np.array([-2, 3])\n", + "yfit = a * xfit + b\n", + " \n", + "p.plot(xfit, yfit, '--', label='y={:.2f}x + {:.2f}'.format(a,b))\n", + "p.legend(prop={'size':18})\n", + "\n", + "p.show()" + ] + }, + { + "cell_type": "markdown", + "id": "83925115-c9fd-482a-a718-d27050661683", + "metadata": {}, + "source": [ + "## Volcano plots\n", + "\n", + "The Oxygen evolution reaction (OER) overpotential tends to form a volcano relationship with the O-OH adsorption energy difference. \n", + "\n", + "First we will use scaling relations between OH and OOH to estimate the OOH adsorption energy.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f367403d-e24d-47fc-b0f5-357dda0f01d3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pylab as plt\n", + "import numpy as np\n", + "\n", + "x_data = dataframe_together['reaction_energy_OH']\n", + "y_data = dataframe_together['reaction_energy_O']\n", + "\n", + "def get_overpotential(dGOH, dGO, dGOOH):\n", + " \"\"\"\n", + " ideal step = 1.23 eV\n", + " \n", + " _O2_\n", + " _OOH_| -> step4\n", + " _O_| -> step3\n", + " _OH_| -> step2 \n", + " _H2O_| -> step1\n", + " \"\"\"\n", + " step1 = dGOH\n", + " step2 = dGO - dGOH\n", + " step3 = dGOOH - dGO\n", + " step4 = 4 * 1.23 - dGOOH\n", + " #step_energies = [step1, step2, step3, step4]\n", + " step_energies = [step2, step3] # Only consider step2 and step 3 for simplicity\n", + " overpotential = np.max(step_energies) - 1.23\n", + " return overpotential\n", + "\n", + "def ooh_oh_scaling(doh):\n", + " \"\"\"Oxide scaling for *OH vs *OOH (from previous studies)\"\"\"\n", + " dooh = 0.8443 * doh + 3.136\n", + " return dooh\n", + "\n", + "i = 0\n", + "materials = dataframe_together['surface_composition'].values\n", + "facets = dataframe_together['facet'].values\n", + "\n", + "p.figure(figsize=(8,8))\n", + "for dEOH, dEO in zip (x_data, y_data):\n", + " dGO = dEO - 0.014 # Free energy correction O (from previous studies)\n", + " dGOH = dEOH + 0.2975 # Free energy correction OH (from previous studies)\n", + " X = dGO - dGOH\n", + " dGOOH = ooh_oh_scaling(dGOH)\n", + " \n", + " overpotential = get_overpotential(dGOH, dGO, dGOOH)\n", + " \n", + " plt.plot(X, overpotential, 'bo')\n", + " plt.annotate(materials[i] + str(facets[i]), (X, overpotential), fontsize=12)\n", + " i += 1\n", + " \n", + "\n", + "c = 0.8443 \n", + "d = 3.136\n", + "\n", + "x = np.linspace(0, 2.5, 1000) # O - OH\n", + "\n", + "# Step 2 and 3 as a function of O-OH energy\n", + "# using OOH vs OH and O vs OH scaling relations\n", + "\n", + "step_2 = x - 1.23 # OH -> O step\n", + "step_3 = (c-a) * (x-b) / (a-1) + d - b - 1.23 # O -> OOH step\n", + "\n", + "\n", + "volcano_line = []\n", + "for i in range(len(x)):\n", + " volcano_line += [np.max([step_2[i], step_3[i]])]\n", + "\n", + "plt.plot(x, step_2, '--', color='r', label='step2 OH->O')\n", + "plt.plot(x, step_3, '--', color='g', label='step3 O->OOH')\n", + "\n", + "plt.plot(x, volcano_line, '--', color='k', label='max[step2, step3]')\n", + "\n", + "plt.ylim(1.2, 0.15)\n", + "plt.xlim(0.8, 2.2)\n", + "plt.legend()\n", + "plt.title('OER Volcano')\n", + "plt.xlabel('$\\Delta G_{O} - \\Delta G_{OH}$', fontsize=16)\n", + "plt.ylabel('Overpotential (V)', fontsize=16)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cb0acf96-81fe-4581-8a7c-88463a7fc870", + "metadata": {}, + "source": [ + "## Visualizing COHP and ICOHP curves\n", + "\n", + "In the example below we will use the `aloha` Python code, developed by SUNCAT postdoc Ara Cho, to plot the COHP + ICOHP curves for bulk RuO2\n", + "\n", + "To install the code do:\n", + "```\n", + " git clone https://github.com/aracho12/aloha.git\n", + "```\n", + "and add the directory to $PYTHONPATH. Also install dependencies: \n", + "```\n", + " pip install monty\n", + " pip install mendeleev\n", + " ``` \n", + "The code takes the Lobster output file `COHPCAR.lobster` and the VASP `POSCAR` files as input (example files are in tutorial folder)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "8664496a-7264-4c7f-8a3a-6160e566cc9f", + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'POSCAR'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [11]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mase\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mio\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m read\n\u001b[0;32m----> 2\u001b[0m atoms \u001b[38;5;241m=\u001b[39m \u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPOSCAR\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m view(atoms, viewer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mngl\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m~/Dropbox/SUNCAT/software/ase/ase/io/formats.py:773\u001b[0m, in \u001b[0;36mread\u001b[0;34m(filename, index, format, parallel, do_not_split_by_at_sign, **kwargs)\u001b[0m\n\u001b[1;32m 770\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(_iread(filename, index, \u001b[38;5;28mformat\u001b[39m, io, parallel\u001b[38;5;241m=\u001b[39mparallel,\n\u001b[1;32m 771\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs))\n\u001b[1;32m 772\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 773\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m_iread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mslice\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mio\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 774\u001b[0m \u001b[43m \u001b[49m\u001b[43mparallel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparallel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Dropbox/SUNCAT/software/ase/ase/parallel.py:275\u001b[0m, in \u001b[0;36mparallel_generator..new_generator\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 269\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(generator)\n\u001b[1;32m 270\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_generator\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 271\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (world\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m 272\u001b[0m args \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(args[\u001b[38;5;241m0\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mserial\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m) \u001b[38;5;129;01mor\u001b[39;00m\n\u001b[1;32m 273\u001b[0m \u001b[38;5;129;01mnot\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mparallel\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m)):\n\u001b[1;32m 274\u001b[0m \u001b[38;5;66;03m# Disable:\u001b[39;00m\n\u001b[0;32m--> 275\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m generator(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m result\n\u001b[1;32m 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n", + "File \u001b[0;32m~/Dropbox/SUNCAT/software/ase/ase/io/formats.py:830\u001b[0m, in \u001b[0;36m_iread\u001b[0;34m(filename, index, format, io, parallel, full_output, **kwargs)\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m io\u001b[38;5;241m.\u001b[39macceptsfd:\n\u001b[1;32m 829\u001b[0m mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m io\u001b[38;5;241m.\u001b[39misbinary \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m--> 830\u001b[0m fd \u001b[38;5;241m=\u001b[39m \u001b[43mopen_with_compression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 831\u001b[0m must_close_fd \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 832\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m~/Dropbox/SUNCAT/software/ase/ase/io/formats.py:588\u001b[0m, in \u001b[0;36mopen_with_compression\u001b[0;34m(filename, mode)\u001b[0m\n\u001b[1;32m 585\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lzma\u001b[38;5;241m.\u001b[39mopen(filename, mode)\n\u001b[1;32m 586\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 587\u001b[0m \u001b[38;5;66;03m# Either None or unknown string\u001b[39;00m\n\u001b[0;32m--> 588\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'POSCAR'" + ] + } + ], + "source": [ + "from ase.io import read\n", + "atoms = read('POSCAR')\n", + "\n", + "view(atoms, viewer='ngl')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "a76e4caa-75e3-4735-9482-40ab3824210f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "label ele1 idx1 ele2 idx2 pair -ICOHP distance\n", + " 1 O 2 Ru 0 O2(p)-Ru0(d) 2.01519 1.955089\n", + " 2 O 3 Ru 0 O3(p)-Ru0(d) 2.01522 1.955089\n", + " 3 O 4 Ru 0 O4(p)-Ru0(d) 1.73585 1.994772\n", + " 4 O 4 Ru 0 O4(p)-Ru0(d) 1.73586 1.994771\n", + " 5 O 5 Ru 0 O5(p)-Ru0(d) 1.73594 1.994763\n", + " 6 O 5 Ru 0 O5(p)-Ru0(d) 1.73594 1.994762\n", + " 7 O 2 Ru 1 O2(p)-Ru1(d) 1.73571 1.994765\n", + " 8 O 2 Ru 1 O2(p)-Ru1(d) 1.73573 1.994766\n", + " 9 O 3 Ru 1 O3(p)-Ru1(d) 1.73573 1.994765\n", + " 10 O 3 Ru 1 O3(p)-Ru1(d) 1.73573 1.994766\n", + " 11 O 4 Ru 1 O4(p)-Ru1(d) 2.01555 1.955082\n", + " 12 O 5 Ru 1 O5(p)-Ru1(d) 2.01555 1.955090\n", + "\t\t\t\t-ICOHP sum:21.94800\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Figure saved as cohp.png\n", + "Data saved as cohp.tsv\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from aloha.cohpplot import * \n", + "Cohpplot(sum_labels=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4af0d9c8-54f5-4225-ac07-e09638e84d48", + "metadata": {}, + "source": [ + "## Challenge: Inspect the correlation between ICOHP and O and OH adsorption energies\n", + "\n", + "As discussed in the paper highlighted above, the intrgrated Crystal orbital Hamiltonian population (ICOHP) for the Metal-Oxygen bonds in the bulk oxide is an important descriptor for O and OH adsorption energetics. Test the correlation yourself by downloading COHP data from here: https://github.com/SUNCAT-Center/CatHub/tutorials/SUNCAT_summer_institute_2023/\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "00f8ca71-728d-4904-8458-e483d1e87680", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import pylab as p\n", + "import json\n", + "\n", + "p.rcdefaults()\n", + "bulk_icohp_data = json.load(open('bulk_COHP_data.json', 'r'))\n", + "dataframe_filter= dataframe[(dataframe['facet']=='100')]\n", + "p.figure(figsize=(10,10))\n", + "i = 0\n", + "for structure, data in bulk_icohp_data.items():\n", + " comp = structure.split('_')[1]\n", + " df = dataframe_filter[(dataframe_filter['surface_composition']== comp + '-rutile')]\n", + " if len(df) == 0:\n", + " continue\n", + " E_OH = df[df[\"equation\"] =='H2O(g) - 0.5H2(g) + * -> HO*']['reaction_energy'].values\n", + " E_O = df[df[\"equation\"] =='H2O(g) - H2(g) + * -> O*']['reaction_energy'].values\n", + " icohp = data['integral']\n", + " if i == 0:\n", + " p.plot(-icohp, E_O, 'bo', label='O')\n", + " p.plot(-icohp, E_OH, 'ro', label='OH')\n", + " else:\n", + " p.plot(-icohp, E_O, 'bo')\n", + " p.plot(-icohp, E_OH, 'ro')\n", + " p.annotate(comp, (-icohp, E_O), fontsize=12)\n", + " p.annotate(comp, (-icohp, E_OH), fontsize=12)\n", + " i += 1\n", + "p.xlabel('Bulk M-O COHP (eV)', fontsize=16)\n", + "p.ylabel('$\\Delta$E (eV)', fontsize=16)\n", + "p.legend()\n", + "p.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "327e39bb-0992-48c3-b8ca-61826da8db5c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/ACS_fall_2023/3_experiment.ipynb b/tutorials/ACS_fall_2023/3_experiment.ipynb new file mode 100644 index 0000000..0562abc --- /dev/null +++ b/tutorials/ACS_fall_2023/3_experiment.ipynb @@ -0,0 +1,540 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6d2f8aee-7587-4c8c-93f5-aed754b413b9", + "metadata": {}, + "source": [ + "## Exercise 3: Experimental data\n", + "In this exercise we will access experimental datasets on the catalysis-hub server (which is only available through Python API so far).\n", + "\n", + "### Command line interface (terminal)\n", + "\n", + "For this exercise we will start by using the cathub cli. Go to your terminal and type:\n", + "\n", + "```\n", + "$ cathub exp\n", + "```\n", + "And you should get a pop-up opening in your browser. The table lists the experimental datasets, where you can select the pub_id see more details:\n", + "```\n", + "$ cathub exp \n", + "```\n", + "\n", + "### Python API:\n", + "\n", + "Query the experimental database via the Python API, using public access (read only!) password.\n", + "\n", + "Experimental tables include: `material, sample, xps, xrd and echemical`.\n", + "\n", + "First we will examine the 'material' table:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e897f8c1-dc74-4d89-87ad-6dfa4a928ab8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mat_id pub_id composition arrangement icsd_ids icdd_ids \\\n", + "0 177 HubertAcidic2020 Y2Ru2O7 None None [00-028-1456] \n", + "1 178 HubertAcidic2020 Nd2Ru2O7 None None [00-028-0673] \n", + "2 179 HubertAcidic2020 Gd2Ru2O7 None None [00-028-0425] \n", + "3 180 HubertAcidic2020 Bi2Ru2O7 None None [00-065-0223] \n", + "4 181 HubertAcidic2020 RuO2 None None [00-040-1290] \n", + "\n", + " space_group lattice_parameter morphology notes \n", + "0 Fd-3m 1.014 nanoparticle None \n", + "1 Fd-3m 1.032 nanoparticle None \n", + "2 Fd-3m 1.023 nanoparticle None \n", + "3 Fd-3m 1.025 nanoparticle None \n", + "4 4/mmm 0.45430000000000004 nanoparticle None \n" + ] + } + ], + "source": [ + "from cathub.experimental.data_interface import *\n", + "DB = ExpSQL(user='expvisitor', password='99Ny81eG') # read only access\n", + "\n", + "dataframe_mat = DB.get_dataframe(table='material', pub_id='HubertAcidic2020')\n", + "print(dataframe_mat)\n" + ] + }, + { + "cell_type": "markdown", + "id": "b44f18b5-52d2-40b4-afab-0b04cfbad579", + "metadata": {}, + "source": [ + "## Sample table\n", + "This table contains information about catalyst material, support as well as electrochemical testing metrics:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c06bce36-a89e-4564-b70b-09e8f4556400", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['sample_id', 'mat_id', 'pub_id', 'pH', 'DOI', 'notes', 'K-edge',\n", + " 'L-edge', 'XPS_ID', 'sample', 'reactor', 'reactant', 'reaction',\n", + " 'CV_end_ID', 'ECSA(cm2)', 'binder_ID', 'replicate', 'morphology',\n", + " 'Unnamed:_61', 'Unnamed:_62', 'XPS_spectra', 'XRD(CuKa)ID',\n", + " 'arrangement', 'composition', 'space_group', 'CV_intial_ID',\n", + " 'dataset_name', 'inputer_name', 'substrate_ID', 'C_1s_Shift(eV)',\n", + " 'electrolyte_ID', 'stability_test', 'time_tested(h)',\n", + " 'XPS_post_test_ID', 'charge_passed(C)', 'stability_test_ID',\n", + " 'AFM_roughness(cm2)', 'binder_amount(w/w%)', 'geometric_area(cm2)',\n", + " 'composition_post_test', 'conductive_support_ID',\n", + " 'total_loading(mg/cm2)', 'BET_surface_area(m2/g)',\n", + " 'in_progress(1=yes,0=no)', 'lattice_parameter_a(nm)',\n", + " 'thin_film_thickness(nm)', 'ECSA_determination_method',\n", + " 'Oxidation_States_from_XPS', 'applied_potential(V_vs_RHE)',\n", + " 'Surface_Composition_from_XPS', 'flow_rate/rotation_rate(rpm)',\n", + " 'onset_potential(+/-1_mA/cm2)', 'onset_potential(+/-10_mA/cm2)',\n", + " 'onset_potential(+/-0.1_mA/cm2)', 'AD_measured_metal_loading(nmol)',\n", + " 'onset_potential(+/-0.01_mA/cm2)', 'onset_potential(+/-0.05_mA/cm2)',\n", + " 'applied_current_density(mA_cm-2)', 'electrolyte_concentration(mol/L)',\n", + " 'pretest/posttest_CV_corrections?', 'conductive_support/catalyst_ratio',\n", + " 'double_layer_capacitance(mF_/_cm2)',\n", + " 'final_measured_metal_loading(nmol)',\n", + " '-max_MT_limited_current_density(mA/cm2)'],\n", + " dtype='object')\n", + "-----\n", + " reaction composition conductive_support_ID onset_potential(+/-0.1_mA/cm2)\n", + "0 OER Y2Ru2O7 vulcan C 1.457\n", + "1 OER Y2Ru2O7 vulcan C 1.12\n", + "2 OER Nd2Ru2O7 vulcan C 1.489\n", + "3 OER Nd2Ru2O7 vulcan C -\n", + "4 OER Gd2Ru2O7 vulcan C 1.486\n", + "5 OER Gd2Ru2O7 vulcan C 1.435\n", + "6 OER Bi2Ru2O7 vulcan C 1.479\n", + "7 OER Bi2Ru2O7 vulcan C 0.981\n", + "8 OER RuO2 vulcan C 1.448\n", + "9 OER RuO2 vulcan C 0.785\n" + ] + } + ], + "source": [ + "dataframe_sample = DB.get_dataframe(table='sample', pub_id='HubertAcidic2020')\n", + "print(dataframe_sample.columns)\n", + "print('-----')\n", + "print(dataframe_sample[['reaction', 'composition', 'conductive_support_ID', 'onset_potential(+/-0.1_mA/cm2)']])" + ] + }, + { + "cell_type": "markdown", + "id": "bc24f10e-792a-4170-9966-b5dfe582b26c", + "metadata": {}, + "source": [ + "### Plotting CV curves\n", + "\n", + "As a final example we will plot CV (current / voltage) curves from the 'echemical' table:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d0a8affe-b0bb-49f7-ae08-734d140b2897", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['id', 'type', 'total_time', 'time', 'potential', 'current',\n", + " 'sample_id'],\n", + " dtype='object')\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + " \n", + " Loading BokehJS ...\n", + "
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\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
  • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
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\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
  • \\n\"+\n \"
  • use INLINE resources instead, as so:
  • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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+ ], + "source": [ + "from bokeh.plotting import figure, show\n", + "from bokeh.io import output_notebook\n", + "\n", + "dataframe_echemical = DB.get_dataframe(table='echemical', pub_id='HubertAcidic2020')\n", + "print(dataframe_echemical.columns)\n", + "\n", + "dataframe_echemical = dataframe_echemical.set_index('sample_id').join(\n", + " dataframe_sample.set_index('sample_id'))\n", + "\n", + "output_notebook()\n", + "figure = plot_cvs(dataframe_echemical)\n", + "show(figure)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/tutorials/ACS_fall_2023/COHPCAR.lobster b/tutorials/ACS_fall_2023/COHPCAR.lobster new file mode 100644 index 0000000..5f9cefc --- /dev/null +++ b/tutorials/ACS_fall_2023/COHPCAR.lobster @@ -0,0 +1,1248 @@ +ESCALE ; pCOHP file generated by LOBSTER. Energy is shifted such that the Fermi level lies at 0 eV. + 445 2 801 -1.83727e+01 2.16774e+01 6.65861e+00 +Average +No.1:O3->Ru1(1.9550890026876564) +No.1:O3[2s]->Ru1[5s](1.9550890026876564) +No.1:O3[2p_y]->Ru1[5s](1.9550890026876564) +No.1:O3[2p_z]->Ru1[5s](1.9550890026876564) +No.1:O3[2p_x]->Ru1[5s](1.9550890026876564) +No.1:O3[2s]->Ru1[4p_y](1.9550890026876564) +No.1:O3[2p_y]->Ru1[4p_y](1.9550890026876564) +No.1:O3[2p_z]->Ru1[4p_y](1.9550890026876564) +No.1:O3[2p_x]->Ru1[4p_y](1.9550890026876564) +No.1:O3[2s]->Ru1[4p_z](1.9550890026876564) +No.1:O3[2p_y]->Ru1[4p_z](1.9550890026876564) +No.1:O3[2p_z]->Ru1[4p_z](1.9550890026876564) +No.1:O3[2p_x]->Ru1[4p_z](1.9550890026876564) +No.1:O3[2s]->Ru1[4p_x](1.9550890026876564) +No.1:O3[2p_y]->Ru1[4p_x](1.9550890026876564) +No.1:O3[2p_z]->Ru1[4p_x](1.9550890026876564) +No.1:O3[2p_x]->Ru1[4p_x](1.9550890026876564) +No.1:O3[2s]->Ru1[4d_xy](1.9550890026876564) +No.1:O3[2p_y]->Ru1[4d_xy](1.9550890026876564) 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