Forecasting of in situ electron energy loss spectroscopy

Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency variables. These 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 short-term memory model (LSTM) forecasting situ electron energy loss spectroscopy (EELS) data, one richest analytical probes materials and chemical systems. We key considerations data collection, preprocessing, training, validation, benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, comment such may integrate with AI-guided instrumentation high-speed experimentation.