Normalizing flows for atomic solids

We present a machine-learning approach, based on normalizing flows, for modelling atomic solids. Our model transforms an analytically tractable base distribution into the target solid without requiring ground-truth samples training. report Helmholtz free energy estimates cubic and hexagonal ice modelled as monatomic water well truncated shifted Lennard-Jones system, find them to be in excellent agreement with literature values from established baseline methods. further investigate structural properties show that are nearly indistinguishable ones obtained molecular dynamics. results thus demonstrate flows can provide high-quality need multi-staging.