Forecasting of in situ electron energy loss spectroscopy

8Citations
Citations of this article
29Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

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.

Cite

CITATION STYLE

APA

Lewis, N. R., Jin, Y., Tang, X., Shah, V., Doty, C., Matthews, B. E., … Spurgeon, S. R. (2022). Forecasting of in situ electron energy loss spectroscopy. Npj Computational Materials, 8(1). https://doi.org/10.1038/s41524-022-00940-2

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free