arXiv Machine Learning
Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction
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Packora: Systematic Design for Generative Molecular Crystal Structure Prediction
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Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction
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FastCSP: Accelerated Molecular Crystal Structure Prediction with Universal Model for Atoms
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EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations
EP-Flow introduces a new framework for predicting disordered crystal structures without requiring site-level disorder annotations. It uses an Occupancy Distribution Matrix (ODM) to represent continuous site-by-species occupancies and enforces constraints through a transportation polytope. The method jointly generates occupancies, fractional coordinates, and lattice parameters, achieving state‑of‑the‑art performance on formula‑conditioned disordered CSP benchmarks and recovering chemically meaningful local disorder patterns.