Park Woon Bae, Chung Jiyong, Jung Jaeyoung, Sohn Keemin, Singh Satendra Pal, Pyo Myoungho, Shin Namsoo, Sohn Kee-Sun
Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul 143-747, Republic of Korea.
Laboratory of Big-data Applications for Public Sector, Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul 156-756, Republic of Korea.
IUCrJ. 2017 Jun 13;4(Pt 4):486-494. doi: 10.1107/S205225251700714X. eCollection 2017 Jul 1.
A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.
介绍了一种基于卷积神经网络（CNN）的深度机器学习技术。该技术已用于根据晶体系统、消光群和空间群对粉末X射线衍射（XRD）图谱进行分类。收集了约150000个粉末XRD图谱，并将其用作CNN的输入，无需进行手工工程处理，从而获得了一种合适的CNN架构，该架构能够确定晶体系统、消光群和空间群。与传统的粉末XRD图谱分析方法形成鲜明对比的是，CNN从不将粉末XRD图谱视为解卷积后的离散峰位置或强度数据，而是将XRD图谱仅仅视为类似于图片的图案。CNN能够解读粉末XRD图谱中人类无法识别的特征。结果，空间群、消光群和晶体系统分类的准确率分别达到了81.14%、83.83%和94.99%。然后，将训练有素的CNN用于未知新型无机化合物的对称性识别。
