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American Chemical Society, Journal of Physical Chemistry Letters, 16(6), p. 3309-3313, 2015

DOI: 10.1021/acs.jpclett.5b01456

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Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Journal article published in 2015 by Matthias Rupp ORCID, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld, O. Anatole von Lilienfeld
This paper is available in a repository.
This paper is available in a repository.

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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.