Predicting Vapor-Liquid Equilibria and Phase Transitions with Machine-Learned Interatomic Potentials

A long-standing goal in classical molecular dynamics is to achieve ‘transferability’ of an interatomic potential, meaning the model can remain accurate in out-of-domain applications and reproduce physical properties over a wide range of phase () space. With the increasing popularity and promise of machine learned interatomic potentials (ML-IAP), this goal is finally within reach and has been recently demonstrated in extreme environments of carbon and iron. The present work has trained state-of-the-art ML-IAPs (SNAP, POD, Allegro) for Al over a wide range of phase regimes (0.2-3.0 and 933-10,000 ) that are challenging to model with any other simulation method. We demonstrate the efficiency of these atomic representations by reaching quantum-mechanical accuracy in small dataset and then perform large-scale molecular dynamics simulations to predict the vapor-liquid phase equilibrium and the critical point. Furthermore, these predictions can be validated against carefully crafted DFT simulations. These demonstrations show how ML tools complement and bolster first-principles calculations for equation of state models by allowing for dynamic predictions of materials in extreme environments.