Inverse design of two-dimensional materials with invertible neural networks

查看全文 作　　者：Victor [1]Fung;Jiaxin [2]Zhang;Guoxiang [3]Hu;[1]P.Ganesh;Bobby [1]G.Sumpter

机构地区：[1]Center for Nanophase Materials Sciences,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA;[2]Computer Science and Mathematics Division,Oak Ridge National Laboratory,Oak Ridge,TN 37831,USA;[3]Department of Chemistry and Biochemistry,Queens College of the City University of New York,Queens,NY 11367,USA

出　　处：《npj Computational Materials》索引2021年第1期,共9页

基　　金：This work was performed at the Center for Nanophase Materials Sciences,which is a US Department of Energy Office of Science User Facility.Support was provided by the Center for Understanding and Control of Acid Gas-Induced Evolution of Materials for Energy(UNCAGE-ME),an Energy Frontier Research Center funded by U.S.Department of Energy,Office of Science,Basic Energy Sciences.VF was also supported by a Eugene P.Wigner Fellowship at Oak Ridge National Laboratory.JZ was supported by the U.S.Department of Energy,Office of Science,Office of Advanced Scientific Computing Research,Applied Mathematics Program;by the Artificial Intelligence Initiative at the Oak Ridge National Laboratory(ORNL).ORNL is operated by UTBattelle,LLC.,for the U.S.Department of Energy under Contract DEAC05-00OR22725;This research used resources of the National Energy Research Scientific Computing Center,supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231. 摘　　要：The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task.To tackle this problem,we propose an inverse design framework(MatDesINNe)utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property.This approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse design.We then apply this framework to the task of band gap engineering in two-dimensional materials,starting with MoS_(2).Within the design space encompassing six degrees of freedom in applied tensile,compressive and shear strain plus an external electric field,we show the framework can generate novel,high fidelity,and diverse candidates with near-chemical accuracy.We extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among others.This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties. 关键词：PROPERTIES INVERSE satisfying

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Inverse design of two-dimensional materials with invertible neural networks

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