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

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.

• UD
  U.S. Department of Energy

  Award: DE-AC02-06CH11357
• KC
  King’s College London
• EC
  European Commission

  Award: ADGLASS FP7
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/L027682/1, EP/L027682/1, EP/L014742/1