标识符
卷积神经网络
鉴定(生物学)
软件
计算机科学
相(物质)
粉末衍射
衍射
材料科学
结晶学
数据挖掘
化学
人工智能
物理
光学
有机化学
生物
植物
程序设计语言
作者
Shouyang Zhang,Bin Cao,Tianhao Su,Yue Wu,Zhenjie Feng,Jie Xiong,Tong‐Yi Zhang
出处
期刊:IUCrJ
[International Union of Crystallography]
日期:2024-06-27
卷期号:11 (4): 634-642
被引量:2
标识
DOI:10.1107/s2052252524005323
摘要
Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.
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