Phase Transitions, Molecular Simulations and Machine Learning

Phase transitions are ubiquitous in nature and play a critical role in a broad range of phenomena from ice
formation in clouds to signaling in biological cells. Often the phase transitions include molecular length
and timescales that are hard to probe in experiments but conducive to the lengthscales accessible in
molecular simulations. However, the timescales to observe these phase transitions can be beyond the
accessible timescales in simulations. Consequently, advanced sampling methods are required to sample
such transitions as well as to extract the key information from those sampling.

One such phase transition of interest in our research is liquid-to-solid transition. We use path sampling
methods to sample these transitions – specifically we use forward flux sampling and transition interface
sampling. While the sampling can be achieve several challenges remain – (i) how do we identify the
reaction coordinates (i.e. combination of order parameters) that best ascertain the direction of the
transition (ii) how do we identify the various structures (known and possibly unknown) that form during
such transitions. To this end, we capitalize on the benefits of artificial intelligence methods. Specifically,
we have used PointNet based approaches to identify various structures. We will also discuss our efforts
for using machine learning based protocols for discovering reaction coordinates from the pathways
sampled in our simulations.