Deciphering the complexities of crystalline state(s) with molecular simulations

Predicting the outcome of a crystallization process remains a long-standing challenge in solid state chemistry. This stems from the subtle interplay between thermodynamics and kinetics that results in a complex crystal energy landscape, spanned by many polymorphs and other metastable intermediates. Molecular simulations are uniquely positioned to unravel this interplay, as they constitute a framework that can compute free energies (thermodynamics), barriers (kinetics), and visualize the crystallization mechanisms at high resolution. We show here how recent progress in computational methods, and their augmentation with Machine Learning, has advanced our ability to predict crystal structure and simulate crystal nucleation. Crystallization processes are underpinned by an interplay between thermodynamics and kinetics, leading to complex energy landscapes spanned by polymorphs and metastable intermediates that are challenging to identify and characterize. In this Review, the authors highlight how recent progress in computational methods, and their augmentation with machine learning, have advanced our ability to predict crystal structures and simulate crystal nucleation.