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...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.展开更多
Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and clo...Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and closed-loop microscope operation.The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization.Here,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 operations.Specifically,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 platforms.These considerations will collectively inform the operationalization of ML in next-generation experimentation.展开更多
基金C.D.,B.E.M.,S.A.,and S.R.S.were supported by the Chemical Dynamics Initiative/Investment,under the Laboratory Directed Research and Development(LDRD)Program at Pacific Northwest National Laboratory(PNNL)PNNL is a multi-program national laboratory operated for the U.S.Department of Energy(DOE)by Battelle Memorial Institute under Contract No.DE-AC05-76RL01830+1 种基金N.L.,Y.J.,X.T.,and V.S.were supported by the Data Intensive Research Enabling Clean Technology(DI-RECT)National Science Foundation(NSF)National Research Traineeship(DGE-1633216)the State of Washington through the University of Washington(UW)Clean Energy Institute and the UW eScience Institute.
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.
文摘Machine learning(ML)has become critical for post-acquisition data analysis in(scanning)transmission electron microscopy,(S)TEM,imaging and spectroscopy.An emerging trend is the transition to real-time analysis and closed-loop microscope operation.The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization.Here,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 operations.Specifically,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 platforms.These considerations will collectively inform the operationalization of ML in next-generation experimentation.
