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arXiv Machine Learning By Qiuliang Liu, Liming Wu, Qi Li, Zhonglong Peng, Chang Chen, Xiaolong Chen, Wenbing Huang, Shifeng Jin

EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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