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Forecasting of in situ electron energy loss spectroscopy

npj Computational Materials 2022年第1期8卷 2400-2408页

作者： Nicholas R.Lewis Yicheng Jin Xiuyu Tang Vidit Shah christina doty Bethany E.Matthews Sarah Akers Steven R.Spurgeon Department of Chemical Engineering University of WashingtonSeattleWA98195USA National Security Directorate Pacific Northwest National LaboratoryRichlandWA99352USA Energy and Environment Directorate Pacific Northwest National LaboratoryRichlandWA99352USA Department of Physics University of WashingtonSeattleWA98195USA

Forecasting models are a central part of many control systems,where high-consequence decisions must be made on long latency control *** models are particularly relevant for emerging artificial intelligence(AI)-guided instrumentation,in which prescriptive knowledge is needed to guide autonomous *** 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 *** describe key considerations for data collection,preprocessing,training,validation,and benchmarking,showing how this approach can yield powerful predictive insight into order-disorder phase ***,we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.

关键词： consequence autonomous transitions

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Machine learning for automated experimentation in scanning transmission electron microscopy

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npj Computational Materials 2023年第1期9卷 25-40页

作者： Sergei V.Kalinin Debangshu Mukherjee Kevin Roccapriore Benjamin J.Blaiszik Ayana Ghosh Maxim A.Ziatdinov Anees Al-Najjar christina doty Sarah Akers Nageswara S.Rao Joshua C.Agar Steven R.Spurgeon Department of Materials Science and Engineering University of TennesseeKnoxvilleTN 37996USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Argonne National Laboratory Data Science and Learning DivisionChicagoIL 60439USA University of Chicago GlobusChicagoIL 60637USA National Security Directorate Pacific Northwest National LaboratoryRichlandWA 99352USA Department of Materials Science and Engineering Drexel UniversityPhiladelphiaPA 19104USA Department of Physics University of WashingtonSeattleWA 98195USA

Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and *** emerging trend is the transition to real-time analysis and closed-loop microscope *** effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and ***,we discuss the associated challenges with the transition to active ML,including sequential data analysis and out-of-distribution drift effects,the requirements for edge operation,local and cloud data storage,and theory in the loop ***,we discuss the relative contributions of human scientists and ML agents in the ideation,orchestration,and execution of experimental workflows,as well as the need to develop universal hyper languages that can apply across multiple *** considerations will collectively inform the operationalization of ML in next-generation experimentation.

关键词： optimization automated execution

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