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1篇 您的检索式：作者名="Nicholas R.Lewis"

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		题名	作者	年代	出处	被引量
	1	Forecasting of in situ electron energy loss spectroscopy显示文摘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.	Nicholas R.Lewis Yicheng Jin Xiuyu Tang Vidit Shah Christina Doty Bethany E.Matthews Sarah Akers Steven R.Spurgeon	2022	npj Computational Materials2022，，1：	1

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