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深度学习可以从多相粉末衍射模式快速识别新的准晶体

Hirotaka Uryu¹, Tsunetomo Yamada¹, Koichi Kitahara^2,3

¹Department of Applied Physics, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo, 125-8585, Japan.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

|November 15, 2023

概括

研究人员开发了一种深度学习方法,从粉末X射线衍射模式中快速识别新的准晶体. 这种人工智能方法实现了超过92%的准确性,加速了像Al-Si-Ru准晶体这样的新材料的发现.

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.
人工智能的人工智能

背景情况:

自发现以来,已经发现了100多个稳定的准晶体.
传输电子显微镜 (TEM) 是准晶体验证的标准但劳动密集型方法.
快速和自动粉末X射线衍射 (PXRD) 是有效发现新的准晶体所需的.

研究的目的:

开发一种快速的,自动化的技术,用于识别粉末衍射模式中的准晶相.
为了证明深度学习 (DL) 在复杂的多阶段样本中的阶段识别的有效性.
为了促进新型准晶体的发现.

主要方法:

利用深度神经网络 (DNN) 在人工生成的多相PXRD模式上进行训练.
应用受过训练的DL分类器来选440个实际粉末模式的数据集.
专注于识别准晶相,即使与其他晶相混合.

主要成果:

在从实际的PXRD模式中识别准晶体时,DL分类器实现了超过92%的准确性.
从多相粉样中成功识别出一种新的Al-Si-Ru准晶体.
证明了DL在复杂的衍射数据中区分微妙的准晶体特征的能力.

结论:

深度学习提供了一种强大而快速的工具,用于从PXRD数据中识别未知的阶段.
这种由人工智能驱动的方法显著加速了对新型准晶体的搜索.
该方法即使在具有挑战性的多相样本中也是有效的,而人类专家可能会遇到困难.

关键词:

深度神经网络是一个神经网络.半晶体的半晶体是icosahedral的半晶体机器学习是机器学习.阶段识别阶段识别粉末X射线衍射粉末的X射线.

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