title: Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces.
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.
authors: King's College London, Physics Department, Strand, London WC2R 2LS, United Kingdom.
authors: Zhenwei
authors: Z
authors: Li
authors: King's College London, Physics Department, Strand, London WC2R 2LS, United Kingdom.|Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom.
authors: James R
authors: JR
authors: Kermode
authors: King's College London, Physics Department, Strand, London WC2R 2LS, United Kingdom.|CENMAT-UTS, Via Alfonso Valerio 2, 34127 Trieste, Italy.
authors: Alessandro
authors: A
authors: De Vita