Machine Learning for Autonomous Crystal Structure Identification

We present a machine learning technique to discover relevant structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to their local topology. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by identifying relevant structural features in a simulation of colloidal crystallization, some of which are missed by standard techniques.