Estimating Gibbs free energies via isobaric-isothermal flows

P Wirnsberger and B Ibarz and G Papamakarios, MACHINE LEARNING-SCIENCE AND TECHNOLOGY, 4, 035039 (2023).

DOI: 10.1088/2632-2153/acefa8

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.

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