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Bayesian optimization for ground state conformers
License: MIT License
This project forked from globus-labs/conformer-optimization
Bayesian optimization for ground state conformers
License: MIT License
The BOKEI paper (https://doi.org/10.1039/C9CP06688H) shows the success of using correlated dihedral information.
Ideally, there would be an "automatic" mode that adjusts the initial sampling and total sampling to ensure a full ensemble.
One way to make progress is to have a set, continue to sample (i.e., to see if there's a better global minimum and if the ensemble is complete) and extract some heuristics.
The script should be able to save all geometries (in XYZ or SDF or PDB) format within X kcal/mol of the minimum (e.g. default threshold ~6-10 kcal/mol).
While GFN-FF, GFN2, and ANI are probably the best options for accuracy, it might help for benchmarking to have MMFF94, GAFF, and UFF methods:
Right now, the initial sampling seems to be a normal distribution with ±30° range.
Evaluate:
We have recently established GFN2-derived gas phase preferences.
https://doi.org/10.26434/chemrxiv-2022-h9gjq
CREST allows gfnff minimization with gfn2 energies. This would be a useful option to balance speed and energy accuracy (e.g., also gfn2 geometries vs. dft energies, etc.)
For now, the code uses UCB with a formula favoring increasing exploration, which might make sense for finding all minima.
Benchmark # of steps required and lowest minima across a subset of the BOKEI test set:
Other interesting acquisition functions in BoTorch:
Implement Cremer-Pople ring sampling techniques:
https://doi.org/10.1021/acs.jcim.0c01144
botorch
implements batch acquisition functions. Many tools would benefit from batch evaluation (e.g., 4, 8, 10, etc. energy and relaxation in parallel).
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