Forecasting of in situ electron energy loss spectroscopy
Forecasting of in situ electron energy loss spectroscopy
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DOI：
10.1038/s41524-022-00940-2
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发表时间：
2022-12
影响因子：
9.7
通讯作者：
Nicholas R. Lewis;Yicheng Jin;Xiuyu Tang;Vidit Shah;Christina Doty;B. Matthews;Sarah Akers;S. Spurgeon
中科院分区：
文献类型：
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作者：
Nicholas R. Lewis;Yicheng Jin;Xiuyu Tang;Vidit Shah;Christina Doty;B. Matthews;Sarah Akers;S. Spurgeon
预测模型是许多控制系统的核心部分，其中必须对长延迟控制变量做出高后果决策。这些模型与新兴的人工智能（AI）引导的仪器特别相关，其中需要规范性知识来指导自主决策。在这里，我们描述了用于预测原位电子能量损失谱（EELS）数据的长短期记忆模型（LSTM）的实现，这是材料和化学系统最丰富的分析探针之一。我们描述了数据收集、预处理、训练、验证和基准测试的关键考虑因素，展示了这种方法如何对有序-无序相变产生强大的预测洞察力。最后，我们评论了这样的模型如何与新兴的人工智能引导仪器集成，以进行强大的高速实验。
Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.