Noncovalent Quantum Machine Learning Corrections to Density Functionals

We present noncovalent quantum machine learning corrections to six physically motivated density functionals with systematic errors. We demonstrate that the missing massively nonlocal and nonadditive physical effects can be recovered by quantum machine learning models. The models seamlessly account f...

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Bibliographic Details
Published inJournal of chemical theory and computation Vol. 16; no. 4; pp. 2647 - 2653
Main Authors Mezei, Pál D, von Lilienfeld, O. Anatole
Format Journal Article
LanguageEnglish
Published United States American Chemical Society 14.04.2020
Subjects
Density
Dispersion
Hydrogen bonding
Machine learning
Systematic errors
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Summary:We present noncovalent quantum machine learning corrections to six physically motivated density functionals with systematic errors. We demonstrate that the missing massively nonlocal and nonadditive physical effects can be recovered by quantum machine learning models. The models seamlessly account for various types of noncovalent interactions and enable accurate predictions of dissociation curves. The correction improves the description of molecular two- and three-body interactions crucial in large water clusters and provides a reasonable atomic-resolution picture about the interaction energy errors of approximate density functionals that can be useful information in the development of more accurate density functionals. We show that given sufficient training instances the correction is more flexible than standard molecular mechanical dispersion corrections, and thus it can be applied for cases where many dispersion corrected density functionals fail, such as hydrogen bonding.
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ISSN:1549-9618
1549-9626
DOI:10.1021/acs.jctc.0c00181