据悉，目前高通量纳米材料发现的瓶颈在于新材料的结构表征速度。尽管现有的机器学习（ML）方法展现出自动处理电子衍射花样（DPs）的潜力，但在高通量实验中，由于DPs是从随机取向的晶体中收集而来，这些方法往往失效。
受人类决策过程的启发，该研究团队开发了一个自动化框架，用于从任意取向的电子衍射花样中自动分类晶体系统。在这项研究中，研究人员采用深度学习技术，训练卷积神经网络来量化和权衡预测的不确定性，进而融合多视图预测，提高分类的准确性。通过使用电子衍射花样的矢量图表示，该框架在测试案例中实现了高达0.94的精度，不仅对噪声表现出良好的鲁棒性，而且在使用实验数据时保持了显著的精度。这项工作凸显了机器学习在加速实验高通量材料数据分析领域的巨大潜能。
附：英文原文
Title: Automated crystal system identification from electron diffraction patterns using multiview opinion fusion machine learning
Author: Chen, Jie, Zhang, Hengrui, Wahl, Carolin B., Liu, Wei, Mirkin, Chad A., Dravid, Vinayak P., Apley, Daniel W., Chen, Wei
Issue&Volume: 2023-11-9
Abstract: A bottleneck in high-throughput nanomaterials discovery is the pace at which new materials can be structurally characterized. Although current machine learning (ML) methods show promise for the automated processing of electron diffraction patterns (DPs), they fail in high-throughput experiments where DPs are collected from crystals with random orientations. Inspired by the human decision-making process, a framework for automated crystal system classification from DPs with arbitrary orientations was developed. A convolutional neural network was trained using evidential deep learning, and the predictive uncertainties were quantified and leveraged to fuse multiview predictions. Using vector map representations of DPs, the framework achieves a testing accuracy of 0.94 in the examples considered, is robust to noise, and retains remarkable accuracy using experimental data. This work highlights the ability of ML to be used to accelerate experimental high-throughput materials data analytics.
DOI: 10.1073/pnas.2309240120
Source: https://www.pnas.org/doi/abs/10.1073/pnas.2309240120