Applying Machine Learning to Structural Analysis using Pythia

The recent explosion of interest and progress in machine learning (ML) methods has driven a proliferation of their application to soft matter systems. ML promises to deliver novel, automatic characterization techniques to solve previously insurmountable problems and it has already been successfully applied in several key areas for both disordered and ordered materials. However, researchers attempting to utilize ML methods often encounter challenges in finding the most appropriate representation of their data. To help alleviate this problem and foster reproducibility in these applications, we present Pythia, an open-source Python library for generating numerical descriptions of particle configurations. Pythia provides a palette of descriptors for users to select from, ranging from the simple to sophisticated. We demonstrate how Pythia can be combined with standard ML methods to quickly identify structures, analyze crystal grains, and study nucleation and growth of complex colloidal crystalline phases—all in a high-throughput manner.