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 comp...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.展开更多
Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to t...Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials,more complex edge reconstructions can be realized through careful control over the synthesis conditions.However,the whole family of synthesizable,reconstructed edges remains poorly studied.Here,we develop a computational approach integrating ensemble-generation,force-relaxation,and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties.展开更多
Metal oxide-based Resistive Random-Access Memory(RRAM)exhibits multiple resistance states,arising from the activation/deactivation of a conductive filament(CF)inside a switching layer.Understanding CF formation kineti...Metal oxide-based Resistive Random-Access Memory(RRAM)exhibits multiple resistance states,arising from the activation/deactivation of a conductive filament(CF)inside a switching layer.Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM.Here a phase-field model is developed,based on materials properties determined by ab initio calculations,to investigate the role of electrical bias,heat transport and defect-induced Vegard strain in the resistive switching behavior.展开更多
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 Programby 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 DEAC05-00OR22725This 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.
文摘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.
基金This manuscript has been authored by UT-Battelle,LLC under Contract No.DE-AC05-00OR22725 with the U.S.Department of Energy.
文摘Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials,more complex edge reconstructions can be realized through careful control over the synthesis conditions.However,the whole family of synthesizable,reconstructed edges remains poorly studied.Here,we develop a computational approach integrating ensemble-generation,force-relaxation,and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties.
基金P.G.was supported by the Center for Nanophase Materials Sciences,which is a DOE Office of Science User Facility.J.-J.W.acknowledges the partial support from the Army Research Office under grant number W911NF-17-1-0462L.Q.Chen acknowledges partial support from the Computational Materials Sciences Program funded by the US Department of Energy,Office of Science,Basic Energy Sciences,under Award Number DE-SC0020145+1 种基金J.-J.W.and L.-Q.C.also acknowledges the partial support from the Donald W.Hamer Foundation through the Hamer Professorship at Penn State.Y.H.H.acknowledges support from National Natural Science Foundation of China under grant number 51802280This manuscript has been authored by UT-Battelle,LLC under Contract No.DE-AC05-00OR22725 with the U.S.Department of Energy.
文摘Metal oxide-based Resistive Random-Access Memory(RRAM)exhibits multiple resistance states,arising from the activation/deactivation of a conductive filament(CF)inside a switching layer.Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM.Here a phase-field model is developed,based on materials properties determined by ab initio calculations,to investigate the role of electrical bias,heat transport and defect-induced Vegard strain in the resistive switching behavior.
