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Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

Zhenwei Li, James R. Kermode, and Alessandro De Vita
Phys. Rev. Lett. 114, 096405 – Published 6 March 2015
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Abstract

We present a molecular dynamics scheme which combines first-principles and machine-learning (ML) techniques in a single information-efficient approach. Forces on atoms are either predicted by Bayesian inference or, if necessary, computed by on-the-fly quantum-mechanical (QM) calculations and added to a growing ML database, whose completeness is, thus, never required. As a result, the scheme is accurate and general, while progressively fewer QM calls are needed when a new chemical process is encountered for the second and subsequent times, as demonstrated by tests on crystalline and molten silicon.

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  • Received 13 May 2014

DOI:https://doi.org/10.1103/PhysRevLett.114.096405

This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Authors & Affiliations

Zhenwei Li1,†, James R. Kermode1,2,*, and Alessandro De Vita1,3

  • 1King’s College London, Physics Department, Strand, London WC2R 2LS, United Kingdom
  • 2Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom
  • 3CENMAT-UTS, Via Alfonso Valerio 2, 34127 Trieste, Italy

  • *j.r.kermode@warwick.ac.uk
  • Present address: Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland.

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Vol. 114, Iss. 9 — 6 March 2015

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