Chemistry and material innovation is undergoing a transformative shift with the integration of advanced computational and experimental technologies over the past few decades.More recently,the advent of automated workf...Chemistry and material innovation is undergoing a transformative shift with the integration of advanced computational and experimental technologies over the past few decades.More recently,the advent of automated workflows,machine learning(ML)tech-niques,and robotic experiments have elevated sci-entific research to unprecedented levels.We have explored the iterative theoretical-experimental par-adigm,leveraged by robotic artificial intelligence(AI)chemists to bridge the gap between high-volume theoretical data and high-dimensional exper-imental data in this review.By combining automated high-throughput computations,ML models,and ro-botic large-scale experiments,this novel protocol aimed to accelerate data-driven chemistry innova-tion and materials discovery.Successful applications achieved by this paradigm include nanomaterials,high-entropy alloy catalysts,optical thin films,and oxygen evolution reaction(OER)catalysts from Martian meteorites.We have highlighted the potential for this paradigmatic evolution to redefine research methodologies and promote the next generation of precise and intelligent chemistry innovation.展开更多
基金the Innovation Program for Quantum Science and Technology,Chinese Academy of Sciences(CASgrant no.2021ZD0303303)+3 种基金the CAS Project for Young Scientists in Basic Research(grant no.YSBR-005)the National Natural Science Foundation of China(NSFCgrant nos.22025304 and 22033007)K.C.Wong Education Foundation,China(grant no.GJTD-2020-15).
文摘Chemistry and material innovation is undergoing a transformative shift with the integration of advanced computational and experimental technologies over the past few decades.More recently,the advent of automated workflows,machine learning(ML)tech-niques,and robotic experiments have elevated sci-entific research to unprecedented levels.We have explored the iterative theoretical-experimental par-adigm,leveraged by robotic artificial intelligence(AI)chemists to bridge the gap between high-volume theoretical data and high-dimensional exper-imental data in this review.By combining automated high-throughput computations,ML models,and ro-botic large-scale experiments,this novel protocol aimed to accelerate data-driven chemistry innova-tion and materials discovery.Successful applications achieved by this paradigm include nanomaterials,high-entropy alloy catalysts,optical thin films,and oxygen evolution reaction(OER)catalysts from Martian meteorites.We have highlighted the potential for this paradigmatic evolution to redefine research methodologies and promote the next generation of precise and intelligent chemistry innovation.
