Methods in PES-Learn: Direct-Fit Machine Learning of Born-Oppenheimer Potential Energy Surfaces.

Ian T Beck, Justin M Turney, Henry F Schaefer

Abstract read

In one paragraph

Article in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Ian T BeckDepartment of Chemistry, Center for Computational Quantum Chemistry, University of Georgia, Athens, GA 30602, USA.ORCID 0009-0007-9826-2381

Justin M TurneyDepartment of Chemistry, Center for Computational Quantum Chemistry, University of Georgia, Athens, GA 30602, USA.

Henry F SchaeferDepartment of Chemistry, Center for Computational Quantum Chemistry, University of Georgia, Athens, GA 30602, USA.ORCID 0000-0003-0252-2083

Funding

United States Department of Energy DE-SC0018412

6 · The paper itself

Abstract

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.

Indexed as

kernel ridge regressionmachine learningneural networkspotential energy surface

Identifiers

PMID41515397

DOI10.3390/molecules31010100

PMCPMC12786853

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.