OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs

TL;DR

This paper presents OrgFlow, a generative model that predicts organic crystal structures from molecular graphs, addressing a key gap in materials science and outperforming existing methods in accuracy and efficiency.

Contribution

Introduces a flow-matching generative model for organic crystal prediction that incorporates molecular connectivity and symmetry, with a curated dataset and preprocessing pipeline.

Findings

Achieves over 10x higher Match Rate than baselines.

Requires fewer sampling steps during inference.

Establishes generative modeling as a practical approach for organic crystals.

Abstract

Crystal structure prediction is a long-standing challenge in materials science, with most data-driven methods developed for inorganic systems. This leaves an important gap for organic crystals, which are central to pharmaceuticals, polymers, and functional materials, but present unique challenges, such as larger unit cells and strict chemical connectivity. We introduce a flow-matching model for predicting organic crystal structures directly from molecular graphs. The architecture integrates molecular connectivity with periodic boundary conditions while preserving the symmetries of crystalline systems. A bond-aware loss guides the model toward realistic local chemistry by enforcing distributions of bond lengths and connectivity. To support reliable and efficient training, we built a curated dataset of organic crystals, along with a preprocessing pipeline that precomputes bonds and edges, substantially reducing computational overhead during both training and inference. Experiments show that our method achieves a Match Rate more than 10 times higher than existing baselines while requiring fewer sampling steps for inference. These results establish generative modeling as a practical and scalable framework for organic crystal structure prediction.