Machine learning models of material properties accelerate materials discovery,reproducing density functional theory calculated results at a fraction of the cost1–6.To bridge the gap between theory and experiments,mac...Machine learning models of material properties accelerate materials discovery,reproducing density functional theory calculated results at a fraction of the cost1–6.To bridge the gap between theory and experiments,machine learning predictions need to be distilled in the form of interpretable chemical rules that can be used by experimentalists.Here we develop a framework to address this gap by combining evolutionary algorithm-powered search with machine-learning surrogate models.We then couple the search results with supervised learning and statistical testing.This strategy enables the efficient search of a materials space while providing interpretable design rules.We demonstrate its effectiveness by developing rules for the design of direct bandgap materials,stable UV emitters,and IR perovskite emitters.Finally,we conclusively show how DARWIN-generated rules are statistically more robust and applicable to a wide range of applications including the design of UV halide perovskites.展开更多
基金This work was supported financially by the US Research Center,A Division of Sony Corporation of America(2018 Sony Research Award Program Ref#2019-0669)the Natural Sciences and Engineering Research Council(NSERC)of Canada.Authors thank Prof.M.Saidaminov from the University of Victoria for fruitful discussions.Computations were performed on the SOSCIP Consortium’s Niagara and MIST computing platforms.SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario,the Province of Ontario,IBM Canada Ltd.,Ontario Centres of Excellence,MITACS,and 15 Ontario academic member institutions.Machine learning models were trained using GPU resources of Northwestern’s QUEST computing cluster.
文摘Machine learning models of material properties accelerate materials discovery,reproducing density functional theory calculated results at a fraction of the cost1–6.To bridge the gap between theory and experiments,machine learning predictions need to be distilled in the form of interpretable chemical rules that can be used by experimentalists.Here we develop a framework to address this gap by combining evolutionary algorithm-powered search with machine-learning surrogate models.We then couple the search results with supervised learning and statistical testing.This strategy enables the efficient search of a materials space while providing interpretable design rules.We demonstrate its effectiveness by developing rules for the design of direct bandgap materials,stable UV emitters,and IR perovskite emitters.Finally,we conclusively show how DARWIN-generated rules are statistically more robust and applicable to a wide range of applications including the design of UV halide perovskites.
