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OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Preprint   Open access

OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs

Mohammadmahdi Vahediahmar, Matthew A McDonald and Feng Liu
ArXiv.org
22 Feb 2026
DOI: https://doi.org/10.48550/arxiv.2602.20195
url
https://doi.org/10.48550/arXiv.2602.20195View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Computer Science - Learning Physics - Materials Science
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.

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