Input files and scripts for benchmarking OpenFF force fields with yammbs
-
Create a new branch starting from the
master
branch. -
Create a new entry in the
submissions
directory, with the general formatYYYY-MM-DD-Name
. For example:mkdir submissions/$(date +%Y-%m-%d)-Sage-2.1.0
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Add a YAML input file in this directory specifying the force field and datasets to use for the run. For example:
forcefield: openff-2.1.0.offxml datasets: - datasets/cache/industry.json
All paths should be relative to the root of the repository, and cached datasets must be used. Force fields can also correspond to built-in force fields recognized by the toolkit (as in the example). Currently only single datasets are supported.
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Push your branch and open a PR.
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Request a review, and get the PR approved.
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Make a comment of the form
/run-optimization-benchmarks path/to/submission [conda-env.yaml]
or/run-torsion-benchmarks path/to-submission [conda-env.yaml]
on the PR. The brackets indicate an optional argument. If the path to the conda environment is omitted, the default environment will be used (devtools/env.yaml). -
Wait for the benchmarks to finish.
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Review the results, and update your submission with a README containing a link to the Zenodo archive created by CI.
- An OpenFF admin (probably your PR reviewer) will need to manually publish the Zenodo entry before you can do this.
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Merge your PR!
This will produce CSV files corresponding to the DDE, RMSD, TFD, and internal-coordinate RMSD (ICRMSD) metrics computed by yammbs.
Submission | Description | DOI |
---|---|---|
Parsley-1.3.1-unconstrained | openff_unconstrained-1.3.1.offxml | 10.5281/zenodo.14172472 |
Sage-2.0.0 | openff_unconstrained-2.0.0.offxml | 10.5281/zenodo.14188644 |
Sage-2.1.0 | openff-2.1.0.offxml | 10.5281/zenodo.14053221 |
Sage-2.1.0-unconstrained | openff_unconstrained-2.1.0.offxml | 10.5281/zenodo.14058464 |
Sage-2.2.1 | openff_unconstrained-2.2.1.offxml | 10.5281/zenodo.14200591 |
Null-0.0.3 | Protein parameter fit, null model v0.0.3, unconstrained | |
Null-0.0.3-Looser-Priors | Protein parameter fit, null model v0.0.3, unconstrained with looser torsion priors |