  With the advent of faster computer processors and especially graphics
processing units (GPUs) over the last few decades, the use of data-intensive
machine learning (ML) and artificial intelligence (AI) has increased greatly,
and the study of crystal nucleation has been one of the beneficiaries. In this
review, we outline how ML and AI have been applied to address four outstanding
difficulties of crystal nucleation: how to discover better reaction coordinates
(RCs) for describing accurately non-classical nucleation situations; the
development of more accurate force fields for describing the nucleation of
multiple polymorphs or phases for a single system; more robust identification
methods for determining crystal phases and structures; and as a method to yield
improved course-grained models for studying nucleation.
