Predicting Emergent Crystalline Structural Order from Building Block Geometry

Quantitatively determining how building block attributes drive materials systems to form ordered target crystals is a fundamental challenge. Addressing this challenge is particularly difficult for systems that exhibit emergent order. Here, we combine inverse design with machine learning to construct a model that correctly classifies the emergent, entropy-driven crystallization of more than ten thousand convex polyhedral shapes into a small number of structures with an accuracy of greater than 90% using only two parameters. Our results demonstrate that the emergent, self-assembly of entropic crystals is controlled by a remarkably small number of parameters, and provides a quantitative model for predicting the expected behavior of colloidal self-assembly experiments.