Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron micr...Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron microscopy.Because ML models are fast once trained,there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness,which creates broad opportunities for rapid learning and information extraction from experiments.Here,we demonstrate such a capability with the development of autonomous and adaptive XRD.By coupling an ML algorithm with a physical diffractometer,this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases.We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times.The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer.Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.展开更多
基金This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231We also acknowledge support from the U.S.Department of Energy,Office of Science,Basic Energy Sciences,under Contract No.DE-AC02-05-CH11231 within the Joint Center for Energy Storage Research(JCESR)program+1 种基金Computing was performed using resources from the Center for Functional Nanomaterials(CFN),which is a U.S.DOE Office of Science User Facility,at Brookhaven National Laboratory under Contract No.DE-SC0012704N.J.S.was supported in part by the National Science Foundation Graduate Research Fellowship under grant#1752814.
文摘Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron microscopy.Because ML models are fast once trained,there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness,which creates broad opportunities for rapid learning and information extraction from experiments.Here,we demonstrate such a capability with the development of autonomous and adaptive XRD.By coupling an ML algorithm with a physical diffractometer,this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases.We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times.The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer.Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.
