arXiv：cs.LG（机器学习，全量分类）· Qiuliang Liu, Liming Wu, Qi Li, Zhonglong Peng, Chang Chen, Xiaolong Chen, Wenbing Huang, Shifeng Jin·· 15 小时前AI 评分38
EP-Flow：无需位点级标注的无序晶体结构预测
EP-Flow: Disordered Crystal Structure Prediction without Site-Level Annotations
AI 导读
EP-Flow 提出用占据分布矩阵（ODM）统一表示有序晶体、固溶体、空位无序与间隙占据，并通过边缘约束流匹配框架将异构多胞体规范化到共享双中心空间，结合 Sinkhorn 逆映射恢复可行占据。
正文
Abstract:Generative models have made rapid progress in ordered crystal structure prediction, yet many functional materials are intrinsically disordered, with substitutional mixing, vacancies, or interstitial species controlling their properties. Existing crystal generators either assume deterministic site occupations or require site-level disorder annotations, which are often unavailable when the chemical formula is the primary input. We formulate disordered crystal structure prediction through an Occupancy Distribution Matrix (ODM), a continuous site-by-species representation that unifies ordered crystals, solid solutions, vacancy disorder, and interstitial occupancy. A valid ODM must satisfy coupled site-wise occupancy, mass-conservation, and non-negativity constraints, placing each sample on a formula-dependent transportation polytope. We propose Entropic Polytope Flow (EP-Flow), a marginal-constrained flow matching framework that canonicalizes heterogeneous polytopes into a shared double-centered space, learns a marginal-preserving flow, and recovers feasible occupancies through a Sinkhorn inverse map. By jointly generating occupancies, fractional coordinates, and lattice parameters, EP-Flow achieves state-of-the-art performance on formula-conditioned disordered CSP benchmarks derived from COD and MPDS, substantially outperforming adapted ordered-crystal generators. Analyses further show that EP-Flow recovers sparse and chemically meaningful local disorder patterns rather than merely matching global composition statistics.
| Subjects: | Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn) |
| Cite as: | arXiv:2610.01315 [cs.LG] |
| (or arXiv:2610.01315v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01315 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Qiuliang Liu [view email]
[v1]
Thu, 1 Oct 2026 08:47:20 UTC (4,021 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org