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在粉末衍射模式上的卷积自我注意神经网络 (CPICANN) 的晶体相标识符

Shouyang Zhang¹, Bin Cao², Tianhao Su¹

¹Materials Genome Institute, Shanghai University, Shanghai 200444, People's Republic of China.

|July 3, 2024

概括

我们开发了一个快速的人工智能工具,CPICANN,用于使用X射线衍射 (XRD) 数据识别晶体相. 与现有的软件相比,这种先进的方法显著提高了材料表征的准确性和速度.

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:

光谱和衍射数据对于材料的表征至关重要,提供了详细的晶体学信息.
目前用于结晶相识别的方法往往耗时,阻碍了快速分析.

研究的目的:

开发一个实时晶体相位识别工具,以克服目前耗时方法的局限性.
使用衍射数据提高材料表征的速度和准确性.

主要方法:

开发一个卷积自我注意神经网络 (CPICANN),用于实时晶体相识别.
在一个大数据集上对模型进行训练,其中包括692,190个模拟粉末X射线衍射 (XRD) 图案,这些图案来自23,073个独特的无机晶体信息文件.

主要成果:

在模拟的XRD模式上,CPICANN在单相识别中取得了很高的准确性:98.5%具有基本信息,87.5%没有.
双相识别准确率达到84.2% (带有基本信息) 和51.5% (没有模拟数据).
在实验环境中,CPICANN展示了80%的准确性,超过了JADE软件的61%准确性.

结论:

与JADE等传统软件相比,CPICANN在晶体相识别方面提供了卓越的性能.
将CPICANN集成到XRD精制软件中,有望显著推进材料表征技术.

关键词:

在CPICANN中,我们可以在X射线中,X射线的衍射效果是不同的.自主表征的自主表征计算建模计算建模卷积式自我注意力神经网络的神经网络的神经网络阶段识别阶段的识别.粉末衍射衍射的方法结构预测结构预测

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