Work

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Year: 2015

Type: article

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.

Source: The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5b01456

Authors: Matthias Rupp, Raghunathan Ramakrishnan, O. Anatole von Lilienfeld

Institution: University of Basel

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green