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[2602.20195] OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs
Summary
The paper presents OrgFlow, a generative model designed to predict organic crystal structures from molecular graphs, addressing a significant gap in materials science.
Why It Matters
This research is crucial as it advances the field of materials science by providing a novel approach to predicting organic crystal structures, which are vital for pharmaceuticals and functional materials. The model's ability to achieve high accuracy with fewer sampling steps could enhance efficiency in material design and discovery.
Key Takeaways
- OrgFlow integrates molecular connectivity with periodic boundary conditions for accurate predictions.
- The model achieves a Match Rate over 10 times higher than existing methods.
- A curated dataset and preprocessing pipeline significantly reduce computational overhead.
- The approach is scalable and practical for organic crystal structure prediction.
- This research addresses unique challenges in organic materials, expanding the capabilities of data-driven methods.
Condensed Matter > Materials Science arXiv:2602.20195 (cond-mat) [Submitted on 22 Feb 2026] Title:OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs Authors:Mohammadmahdi Vahediahmar, Matthew A. McDonald, Feng Liu View a PDF of the paper titled OrgFlow: Generative Modeling of Organic Crystal Structures from Molecular Graphs, by Mohammadmahdi Vahediahmar and 2 other authors View PDF HTML (experimental) 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 tim...
