Machine learning for accuracy in density functional approximations

Voss, Johannes

doi:10.1002/jcc.27366

Title: Machine learning for accuracy in density functional approximations

Journal Article · Fri Apr 26 04:00:00 UTC 2024 · Journal of Computational Chemistry

DOI: https://doi.org/10.1002/jcc.27366 · OSTI ID:2403662

^[1]

SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States). SUNCAT Center for Interface Science and Catalysis; SLAC

Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the predictive power of computationally efficient electronic structure methods, such as density functional theory, to chemical accuracy and to correct for fundamental errors in density functional approaches. In this paper, recent progress in applying machine learning to improve the accuracy of density functional and related approximations is reviewed. Promises and challenges in devising machine learning models transferable between different chemistries and materials classes are discussed with the help of examples applying promising models to systems far outside their training sets.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division (CSGB)

Grant/Contract Number:: AC02-76SF00515

OSTI ID:: 2403662

Journal Information:: Journal of Computational Chemistry, Journal Name: Journal of Computational Chemistry Journal Issue: 21 Vol. 45; ISSN 0192-8651

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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