北京大学深圳研究生院潘锋团队报道了利用深度学习从X射线衍射图中，确定未知化合物的晶体结构。相关研究成果发表在2024年3月13日出版的《美国化学会杂志》。

从实验表征中确定以前看不见的化合物的结构，是材料科学的重要组成部分。它需要搜索符合未知化合物晶格的结构类型的步骤，这使得能够对表征数据进行模式匹配过程，例如X射线衍射（XRD）谱。然而，这一过程通常对领域专业知识提出了很高的要求，从而为计算机驱动的自动化制造了障碍。

该文中，研究人员通过利用由卷积残差神经网络联合组成的深度学习模型来应对这一挑战。该模型的准确性在100种结构类型的60000多种不同化合物的数据集上得到了证明，并且可以在不需要重新训练现有网络的情况下集成其他类别。研究人员还揭示了深度学习黑匣子的操作，并强调了基于XRD谱中的局部和全局特征，来量化未知化合物和结构类型之间相似性的方式。

这种计算工具为高通量实验中发现的材料结构分析自动化开辟了新的途径。

附：英文原文

Title: Crystal Structure Assignment for Unknown Compounds from X-ray Diffraction Patterns with Deep Learning

Author: Litao Chen, Bingxu Wang, Wentao Zhang, Shisheng Zheng, Zhefeng Chen, Mingzheng Zhang, Cheng Dong, Feng Pan, Shunning Li

Issue&Volume: March 13, 2024

Abstract: Determining the structures of previously unseen compounds from experimental characterizations is a crucial part of materials science. It requires a step of searching for the structure type that conforms to the lattice of the unknown compound, which enables the pattern matching process for characterization data, such as X-ray diffraction (XRD) patterns. However, this procedure typically places a high demand on domain expertise, thus creating an obstacle for computer-driven automation. Here, we address this challenge by leveraging a deep-learning model composed of a union of convolutional residual neural networks. The accuracy of the model is demonstrated on a dataset of over 60,000 different compounds for 100 structure types, and additional categories can be integrated without the need to retrain the existing networks. We also unravel the operation of the deep-learning black box and highlight the way in which the resemblance between the unknown compound and a structure type is quantified based on both local and global characteristics in XRD patterns. This computational tool opens new avenues for automating structure analysis on materials unearthed in high-throughput experimentation.

DOI: 10.1021/jacs.3c11852

Source: https://pubs.acs.org/doi/abs/10.1021/jacs.3c11852