The application of machine learning in materials science has yielded several benefits, including the prediction of physical properties and the improvement of experimental efficiency. However, with complex models, such as convolutional neural networks (CNN), learning has become a black box, from which no universal physical knowledge can be obtained. In this study, a highly accurate prediction of the electrical properties of polycrystalline semiconductor thin films is achieved by learning multichannel CNN models from electron backscattering diffraction (EBSD) data, that is band contrasts, grain boundaries, and inverse pole figures. In addition, it examines how the CNN model learned the correlation between the crystallinity, grain boundaries, crystallographic orientation, and carrier mobility by polarizing certain EBSD data and checking the predicted changes in carrier mobility. Physical parameters affecting carrier mobility can be extracted, which is challenging via human image recognition. The methods proposed in this study will not only enable the prediction of electrical properties from EBSD data for all materials but also will contribute to the discovery of complex physical phenomena beyond the limits of human analysis.

中文翻译：

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利用电子背散射衍射的多通道机器学习预测和阐明多晶材料的物理性质

机器学习在材料科学中的应用产生了多种好处，包括物理性质的预测和实验效率的提高。然而，对于复杂的模型，例如卷积神经网络（CNN），学习已成为一个黑匣子，无法从中获得普遍的物理知识。在这项研究中，通过从电子背散射衍射 (EBSD) 数据（即能带对比、晶界和反极图）学习多通道 CNN 模型，实现了对多晶半导体薄膜电性能的高精度预测。此外，它还研究了 CNN 模型如何通过极化某些 EBSD 数据并检查载流子迁移率的预测变化来了解结晶度、晶界、晶体取向和载流子迁移率之间的相关性。可以提取影响载流子迁移率的物理参数，这对于人类图像识别来说是具有挑战性的。本研究中提出的方法不仅能够根据 EBSD 数据预测所有材料的电性能，还将有助于发现超出人类分析极限的复杂物理现象。