Journal
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
Volume 6, Issue 16, Pages 3309-3313Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jpclett.5b01456
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Funding
- Swiss National Science Foundation [PP00P2_138932]
- Swiss National Science Foundation (SNF) [PP00P2_138932] Funding Source: Swiss National Science Foundation (SNF)
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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.
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