Driving and characterizing nucleation of urea and glycine polymorphs in water

TL;DR

This paper uses advanced machine learning-enhanced molecular dynamics simulations to study the nucleation processes of urea and glycine in water, revealing complex polymorphic transitions and stability insights.

Contribution

It introduces a bias-free, machine learning-augmented simulation approach to analyze molecular nucleation with high temporal resolution.

Findings

Multiple polymorph transitions observed

Reaction coordinates are highly non-classical

Polymorph stability relative to liquid state calculated

Abstract

Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights to the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here we employ the machine learning augmented molecular dynamics framework ``Reweighted Autoencoded Variational Bayes for enhanced sampling (RAVE)". We study two molecular systems, urea and glycine in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth liquid-solid transitions of different polymorphs and from these trajectories calculate the polymorph stability relative to the dissolved liquid state. We further observe that the obtained reaction coordinates and transitions are highly non-classical.