Machine Learning: Science and Technology (Jan 2023)

Estimating Gibbs free energies via isobaric-isothermal flows

  • Peter Wirnsberger,
  • Borja Ibarz,
  • George Papamakarios

DOI
https://doi.org/10.1088/2632-2153/acefa8
Journal volume & issue
Vol. 4, no. 3
p. 035039

Abstract

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We present a machine-learning model based on normalizing flows that is trained to sample from the isobaric-isothermal ensemble. In our approach, we approximate the joint distribution of a fully-flexible triclinic simulation box and particle coordinates to achieve a desired internal pressure. This novel extension of flow-based sampling to the isobaric-isothermal ensemble yields direct estimates of Gibbs free energies. We test our NPT -flow on monatomic water in the cubic and hexagonal ice phases and find excellent agreement of Gibbs free energies and other observables compared with established baselines.

Keywords