Microscopic processes governing polymorphic transitions can be highly complex and are non-trivial to sample with molecular simulations. Due to the complexity of the transformation mechanisms, it is often difficult to suggest suitable collective variables that can be used in enhanced sampling methods. Here, we derive collective variables based on a machine learning classification approach of local structural environments. This local information is combined into global classifiers that are used in enhanced sampling, which allows us to drive global phase transformations through changes in local structural motifs. One key advantage of this approach is that to train the classification model, only information within the stable states but not of the transition itself is required. We exemplify our approach by sampling the migration of phase boundaries during polymorphic transitions.
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