Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Rupp, Matthias and Ramakrishnan, Raghunathan and von Lilienfeld, O. Anatole. (2015) Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. Journal of Physical Chemistry Letters, 6 (16). pp. 3309-3313.

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Official URL: http://edoc.unibas.ch/53266/

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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.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Former Organization Units Chemistry > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Chemical Society
Note:Publication type according to Uni Basel Research Database: Journal article
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edoc DOI:
Last Modified:07 Jul 2020 07:55
Deposited On:25 Jan 2017 14:24

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