Automated classification of big X-ray diffraction data using deep learning models 

Jerardo E. Salgado, Samuel Lerman, Zhaotong Du, Chenliang Xu & Niaz Abdolrahim

npj Computational Materials 9: 214 (2023)

Editorial Summary

XRD data：An automated deep learning classifier

Determining the crystal structure of solid and liquid materials is important for understanding their mechanical, electromagnetic and thermodynamic properties. Powder X-ray diffraction (XRD) is an important means of material characterization, encoding information about crystal symmetry, lattice parameters, type, and filling of atoms on nanoscale domains. However, the current classification method requires a lot of human intervention to complete the classification based on comprehensive evaluation of the overall information. There are many variables that affect the shape of an XRD pattern, such as the phase or crystal lattice of the material. Without a known similar structure, it is difficult to characterize the material. In addition, the presence of some small amounts of impurity phases in the sample may make classification more difficult and time-consuming. and inaccuracies. Recent advances in ultrafast synchronized XRD and spectroscopy measurements have generated extremely large data sets from millions of measurements, far exceeding what humans can manually analyze. Therefore, there is an urgent need for adaptive and automatic analysis of XRD data. The performance of currently developed deep learning models on different data sets varies greatly, showing insufficient robustness. A more robust model is needed that can classify dynamic and/or unseen real XRD data obtained from different materials. In this work, a group led by Prof. Niaz Abdolrahim from the School of Mechanical Engineering, University of Rochester, developed a generalized deep learning model for crystal system and space group classification. Considering that the relative peak intensity, distance and order in XRD data indicate symmetry, the researchers investigated whether there is alignment invariance and translation invariance, and based on this, they proposed a no-pool convolutional neural network (NPCNN). Classification was accomplished by characterizing materials based on relative and local inferences between indexed peaks. To enable extensive classification capabilities, the authors also developed a data generation pipeline to build high-quality data sets that incorporates experimental effects on diffraction patterns. The pipeline also has the capability of simulating materials that undergo alloying and/or dynamic experimentation. The researchers succeeded in making the deep learning model achieve state-of-the-art performance. This study provides a valuable platform for developing models of other spectral characterization techniques.

编辑概述

XRD数据：深度学习自动分类器

测定固体和液体材料的晶体结构对于理解它们的机械、电磁和热力学性质是十分重要的。粉末X射线衍射（XRD）是材料表征的重要手段，它编码了关于晶体对称性、晶格参数、类型以及纳米级上原子的填充信息。然而，现在的分类方法需要大量的人为干预，根据总体信息综合评判来完成分类。有许多变量会影响XRD图案的形状，如材料的相或晶格，如果没有已知类似的结构，就很难表征材料。此外，样品中存在的一些少量的杂质相可能会导致分类更加困难、耗时和不准确。超快同步X射线衍射和光谱学测量的最新进展在于从数百万次测量中产生了极大的数据集，远远超过了人类可以手动分析的数据量。因此，对XRD数据进行自适应和自动分析目前存在迫切的需求。目前已开发的深度学习模型在不同数据集下的表现差异极大，表现为鲁棒性不足的特点。因此，我们需要一个更具有鲁棒性的模型，可以对不同材料的动态和/或看不见的真实XRD数据进行分类。在本工作中，来自罗切斯特大学机械工程学院的Niaz Abdolrahim教授小组，开发了一种用于晶体系统和空间群分类的广义深度学习模型。由于XRD数据中的相对峰强度、距离和顺序表征了对称性，研究人员研究了其中是否存在排列不变性和平移不变性，并据此提出了无池化的卷积神经网络（NPCNN），基于索引峰之间的相对和局部推理来表征材料，以此来完成分类工作。为了实现广泛的分类功能，作者还开发了一个数据生成流水线来建立高质量的数据集，该流水线结合了对衍射模式的实验效应，并且具有模拟经过合金化和/或动态实验的材料的能力。最后，研究人员成功使深度学习模型并发挥出了最先进的性能。该研究也为开发其他光谱表征技术模型提供了有效的研究思路。