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Operators in Machine Learning: Response Properties in Chemical Space

Christensen, Anders S. and Faber, Felix A. and von Lilienfeld, Anatole O.. (2018) Operators in Machine Learning: Response Properties in Chemical Space. Journal of Chemical Physics, 150. 064105.

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Official URL: https://edoc.unibas.ch/68681/

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Abstract

The role of response operators is well established in quantum mechanics. We investigate their use for universal quantum machine learning models of response properties in molecules. After introducing a theoretical basis, we present and discuss numerical evidence based on measuring the potential energy's response with respect to atomic displacement and to electric fields. Prediction errors for corresponding properties, atomic forces and dipole moments, improve in a systematic fashion with training set size and reach high accuracy for small training sets. Prediction of normal modes and IR-spectra of some small molecules demonstrates the usefulness of this approach for chemistry.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:Christensen, Anders Steen and von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:AIP Publishing
ISSN:0021-9606
e-ISSN:1089-7690
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:22 Aug 2019 13:56
Deposited On:08 Jul 2019 13:20

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