Decoding defect statistics from diffractograms via machine learning
Decoding defect statistics from diffractograms via machine learning
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DOI：
10.1038/s41524-021-00539-z
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发表时间：
2021-05-17
影响因子：
9.7
通讯作者：
Dingreville, Remi
中科院分区：
文献类型：
--
作者：
Kunka, Cody;Shanker, Apaar;Dingreville, Remi
衍射技术可以有效且无损地探测材料，同时在空间和时间上保持高分辨率。不幸的是，由于难以从衍射图的特征中解码所需的材料信息，这些表征受到限制，有时甚至是错误的。目前，这些特征不是通过人类直觉来全面识别的，因此生成的模型只能预测可用结构信息的子集。在目前的工作中，我们展示了（i）如何计算完全总结衍射图的机器识别特征，以及（ii）如何利用机器学习将这些特征可靠地连接到扩展的结构统计数据集。为了举例说明这个框架，我们评估了由辐照铜的原子模拟生成的虚拟电子衍射图。当基于机器识别的特征而不是人类识别的特征时，我们的机器学习模型不仅预测缺陷群体的单点统计（即密度），而且预测缺陷群体的两点统计（即空间分布）。因此，这项工作表明，输入机器识别特征的机器学习模型显着推进了准确、鲁棒地解码衍射图的现有技术。
Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.