CUED Publications database

Machine learning unifies the modeling of materials and molecules.

Bartók, AP and De, S and Poelking, C and Bernstein, N and Kermode, JR and Csányi, G and Ceriotti, M (2017) Machine learning unifies the modeling of materials and molecules. Sci Adv, 3. e1701816-.

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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.

Item Type: Article
Uncontrolled Keywords: cond-mat.mtrl-sci cond-mat.mtrl-sci physics.chem-ph
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:57
Last Modified: 22 May 2018 05:58