Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations.

Abstract

Controlling crystal growth is a challenge across numerous industries, as the functional properties of crystalline materials are determined during formation and often depend on particle shape. Current approaches rely on expensive, time-consuming experimental studies complemented by exhaustive parameter space simulations, creating significant computational and analytical burdens. Despite machine learning advances in crystal growth for structure-property relationships, applications targeting morphological control remain underdeveloped. Here, we demonstrate how disentangling autoencoders combined with particle aspect ratio and spherical harmonics descriptors can enhance simulation workflows for crystal growth. This approach reveals continuous transformation pathways between different crystal morphologies whilst preserving underlying crystallographic principles. Our method significantly reduces data analytics burdens, shortens design study timelines, and deepens understanding of crystal shape control. This framework enables more efficient exploration of possible crystal morphologies, facilitating the targeted design of crystalline materials with specific functional properties.