用概率模型统一处理晶体结构去噪、相变分类和序参量计算。
A probabilistic framework for crystal structure denoising, phase classification, and order parameters
- 基于原子级概率框架，联合预测相位与序参量。
- 在强噪声、缺陷、高温等复杂条件下仍保持高精度识别。
- 适合材料模拟中需自动分析复杂结构的科研人员使用。
原子模拟产生大量含噪声的结构数据，但如何鲁棒且通用地提取相位标签与连续序参量仍具挑战。现有工具多针对特定原型，且将去噪、分类、序参量构建分步处理。本文提出统一的概率框架，用于分析已知晶体原型的噪声原子构型。模型输出每个原子对各原型的对数几率（logits），并聚合为原子坐标上的标量对数概率（logP）场。其梯度定义保守去噪场，而对数几率提供局部相位标签、原型解析的序参量及通过对数几率差值衡量的不确定性。在Materials Project的AFLOW映射晶格结构上，通过合成位置与弹性扰动训练，测试其在更强噪声、有限温度无序、点缺陷、水-冰共存、二元多形体及冲击压缩钛中的外推性能。单一可微标量模型经去噪后恢复原型身份，能追踪如Bain和Burgers路径等平滑相变过程，并揭示缺陷与相界附近低置信区域。该框架实现集成化与可扩展的复杂原子模拟分析。
原文摘要 · Abstract (English)
Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains challenging. Existing tools are often specialized to a limited set of prototypes and split thermal-noise removal, phase classification, and OP construction into separate steps. Here we present a unified probabilistic framework for analyzing noisy atomic configurations with respect to known crystal prototypes. The model predicts per-atom, per-prototype logits and aggregates them into a scalar log-probability (logP) landscape over atomic coordinates. Its gradient defines a conservative denoising field, while the logits provide local phase labels, prototype-resolved OPs, and ambiguity measures through logit margins. We train on AFLOW-mapped crystalline structures from the Materials Project with synthetic positional and elastic perturbations, then test extrapolation to stronger noise, finite-temperature disorder, point defects, water--ice coexistence, binary polymorphs, and shock-compressed Ti. A single differentiable scalar model recovers prototype identity after denoising, tracks smooth transformations such as Bain and Burgers paths, and exposes low-confidence regions near defects and phase boundaries. This provides an integrated and extensible tool for analyzing complex atomistic simulations.
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