Crystal Structure Prediction with a Geometric Permutation-Invariant Loss Function (2509.00832v1)

Abstract: Crystalline structure prediction remains an open challenge in materials design. Despite recent advances in computational materials science, accurately predicting the three-dimensional crystal structures of organic materials--an essential first step for designing materials with targeted properties--remains elusive. In this work, we address the problem of molecular assembly, where a set $\mathcal{S}$ of identical rigid molecules is packed to form a crystalline structure. Existing state-of-the-art models typically rely on computationally expensive, iterative flow-matching approaches. We propose a novel loss function that correctly captures key geometric molecular properties while maintaining permutation invariance over $\mathcal{S}$. We achieve this via a differentiable linear assignment scheme based on the Sinkhorn algorithm. Remarkably, we show that even a simple regression using our method {\em SinkFast} significantly outperforms more complex flow-matching approaches on the COD-Cluster17 benchmark, a curated subset of the Crystallography Open Database (COD).