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Machine learning unifies the modeling of materials and molecules.

Published version
Peer-reviewed

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Authors

Poelking, Carl 
Bernstein, Noam 

Abstract

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modeling, underpinning our understanding of chemical and materials properties and transformations. We show that a machine-learning model, based on a local description of chemical environments and Bayesian statistical learning, provides a unified framework to predict atomic-scale properties. It captures the quantum mechanical effects governing the complex surface reconstructions of silicon, predicts the stability of different classes of molecules with chemical accuracy, and distinguishes active and inactive protein ligands with more than 99% reliability. The universality and the systematic nature of our framework provide new insight into the potential energy surface of materials and molecules.

Description

Keywords

cond-mat.mtrl-sci, cond-mat.mtrl-sci, physics.chem-ph

Journal Title

Sci Adv

Conference Name

Journal ISSN

2375-2548
2375-2548

Volume Title

3

Publisher

American Association for the Advancement of Science (AAAS)
Sponsorship
Engineering and Physical Sciences Research Council (EP/K014560/1)
Engineering and Physical Sciences Research Council (EP/L014742/1)
Engineering and Physical Sciences Research Council (EP/P022596/1)
Engineering and Physical Sciences Research Council (EP/J010847/1)
Engineering and Physical Sciences Research Council (EP/J022012/1)
European Commission Horizon 2020 (H2020) Research Infrastructures (RI) (676580)