Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces
Published in Molecules, 2025
The release of PES-Learn version 1.0 as an open-source software package for the automatic construction of machine learning models of semi-global molecular potential energy surfaces (PESs) is presented. Improvements to PES-Learn’s interoperability are stressed with new Python API that simplifies workflows for PES construction via interaction with QCSchema input and output infrastructure. In addition, a new machine learning method is introduced to PES-Learn: kernel ridge regression (KRR). The capabilities of KRR are emphasized with examination of select semi-global PESs. All machine learning methods available in PES-Learn are benchmarked with benzene and ethanol datasets from the rMD17 database to illustrate PES-Learn’s performance ability. Fitting performance and timings are assessed for both systems. Finally, the ability to predict gradients with neural network models is presented and benchmarked with ethanol and benzene. PES-Learn is an active project and welcomes community suggestions and contributions.
Recommended citation: Beck, I. T.; Turney, J. M.; Schaefer III, H. F. Methods in PES-Learn: Direct-Fit Machine Learning of Born–Oppenheimer Potential Energy Surfaces. Molecules 2025, 31(1), 100
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