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Journal Article

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules


Rupp,  Matthias
Institute of Physical Chemistry and National Center for Computational Design and Discovery of Novel Materials (MARVEL), Department of Chemistry, University of Basel;
Theory, Fritz Haber Institute, Max Planck Society;

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Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015). Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. The Journal of Physical Chemistry Letters, 6(16), 3309-3313. doi:10.1021/acs.jpclett.5b01456.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0028-2851-9
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 non-linear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to sub-mesoscale lengths.