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使用生成模型确定粉末衍射晶体结构

Qi Li^1,2, Rui Jiao^3,4, Liming Wu^5,6,7

¹The Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing, China.

Nature communications

|August 11, 2025

概括

PXRDGen是一种新的AI工具,可以在几秒钟内从粉末X射线衍射 (PXRD) 数据中准确确定晶体结构. 这种人工智能方法简化了复杂结构的确定,使其可用于各种科学领域.

科学领域:

材料科学材料科学材料科学
晶体学晶体学是指结晶学.
计算化学的计算化学

背景情况:

精确的晶体结构确定对于理解材料特性至关重要.
从粉末X射线衍射 (PXRD) 数据中解决晶体结构通常是复杂和耗时的,需要大量的专业知识.
现有的方法经常面临诸如重叠峰和轻原子定位等挑战.

研究的目的:

介绍PXRDGen,这是一个端到端的神经网络,旨在从PXRD数据自动确定晶体结构.
开发一种工具,大大减少PXRD分析所需的时间和专业知识.
使用PXRD数据提高晶体结构精炼的精度和效率.

主要方法:

PXRDGen使用了深度学习架构,集成了一个预训练的XRD编码器,一个基于扩散/流的结构生成器,以及一个Rietveld改进模块.
该模型从实验稳定的晶体及其相应的PXRD模式中学习了联合结构分布.
该系统在MP-20数据集上进行训练和评估.

主要成果:

对于MP-20数据集中的有效化合物,PXRDGen实现了创纪录的高匹配率,为82% (1样本) 和96% (20样本).
通过PXRDGen实现的根平均平方误差 (RMSE) 接近传统瑞特维尔德精炼的精度极限.
人工智能模型成功地解决了PXRD分析的挑战,包括重叠的峰值分辨率,光原子定位和邻近元素的差异化.

结论:

PXRDGen提供了一个非常准确和快速的解决方案,用于从PXRD数据中确定晶体结构.
由人工智能驱动的方法使晶体结构分析民主化,使其在科学学科中更容易获得.
这种工具有可能通过简化PXRD精炼过程来加速材料发现和研究.

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