Estimating Gibbs free energies via isobaric-isothermal flows
This work provides a novel method for computational chemistry and materials science to directly estimate Gibbs free energies, which is incremental as it extends flow-based sampling to a specific ensemble.
The authors tackled the problem of estimating Gibbs free energies by developing a machine-learning model based on normalizing flows that samples from the isobaric-isothermal ensemble, achieving excellent agreement with established baselines for monatomic water in ice phases.
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.