CUED Publications database

Machine Learning Unifies the Modelling of Materials and Molecules

Bartok, AP and De, S and Poelking, C and Bernstein, N and Kermode, J and Csanyi, G and Ceriotti, M Machine Learning Unifies the Modelling of Materials and Molecules. (Unpublished)

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Abstract

Determining the stability of molecules and condensed phases is the cornerstone of atomistic modelling, underpinning our understanding of chemical and materials properties and transformations. Here 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 provides 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
Subjects: UNSPECIFIED
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:57
Last Modified: 05 Sep 2017 01:51
DOI: