Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Research Database / FORSCHUNGSDATENBANK

Publication 3542512 | Verified

Data Entry: Please note that the research database will be replaced by UNIverse by the end of October 2023. Please enter your data into the system https://universe-intern.unibas.ch. Thanks

Login for users with Unibas email account...

Login for registered users without Unibas email account...

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)

ID	3542512
Author(s)	Rupp, Matthias; Ramakrishnan, Raghunathan; von Lilienfeld, O. Anatole
Author(s) at UniBasel	von Lilienfeld, Anatole
Year	2015
Title	Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Journal	Journal of Physical Chemistry Letters
Volume	6
Number	16
Pages / Article-Number	3309-3313
Abstract	We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within nonlinear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9 k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to submesoscale lengths.
Publisher	American Chemical Society
ISSN/ISBN	1948-7185
edoc-URL	http://edoc.unibas.ch/53266/
Full Text on edoc	Available
Digital Object Identifier DOI	10.1021/acs.jpclett.5b01456
ISI-Number	000360027000013
Document type (ISI)	Article

02/05/2024

Research Database / FORSCHUNGSDATENBANK