Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields.Materials science,in particular,encompasses a variety of experimental and theoretical approaches th...Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields.Materials science,in particular,encompasses a variety of experimental and theoretical approaches that require careful benchmarking.Leaderboard efforts have been developed previously to mitigate these issues.However,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking.This work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility.The platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data submissions.We cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).展开更多
The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and...The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.展开更多
Many technological applications depend on the response of materials to electric fields,but available databases of such responses are limited.Here,we explore the infrared,piezoelectric,and dielectric properties of inor...Many technological applications depend on the response of materials to electric fields,but available databases of such responses are limited.Here,we explore the infrared,piezoelectric,and dielectric properties of inorganic materials by combining highthroughput density functional perturbation theory and machine learning approaches.We computeΓ-point phonons,infrared intensities,Born-effective charges,piezoelectric,and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database.We find 3230 and 1943 materials with at least one far and mid-infrared mode,respectively.展开更多
基金supported by the financial assistance award 70NANB19H117 from the U.S.Department of Commerce,National Institute of Standards and Technologysupported by the U.S.Department of Energy,Office of Science,Basic Energy Sciences,Materials Sciences and Engineering Division,as part of the Computational Materials Sciences Program and Center for Predictive Simulation of Functional Materials+5 种基金supported by the Center for Nanophase Materials Sciences,which is a US Department of Energy,Office of Science User Facility at Oak Ridge National LaboratoryAHR thanks the Supercomputer Center and San Diego Supercomputer Center through allocation DMR140031 from the Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support(ACCESS)program,which is supported by National Science Foundation grants#2138259,#2138286,#2138307,#2137603,and#2138296supported by NIST award 70NANB19H005 and NSF award CMMI-2053929S.H.W.especially thanks to the NSF Non-Academic Research Internships for Graduate Students(INTERN)program(CBET-1845531)for supporting part of the work in NIST under the guidance of K.CA.M.K.acknowledges support from the School of Materials Engineering at Purdue University under startup account F.10023800.05.002support by the Federal Ministry of Education and Research(BMBF)under Grant No.01DM21001B(German-Canadian Materials Acceleration Center).
文摘Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields.Materials science,in particular,encompasses a variety of experimental and theoretical approaches that require careful benchmarking.Leaderboard efforts have been developed previously to mitigate these issues.However,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking.This work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility.The platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data submissions.We cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).
基金K.C.thanks the computational support from XSEDE computational resources under allocation number TGDMR 190095Contributions from K.C.were supported by the financial assistance award 70NANB19H117 from the U.S.Department of Commerce,National Institute of Standards and Technology+3 种基金Contributions by S.M.,K.H.,K.R.,and D.V.were supported by NSF DMREF Grant No.DMR-1629059 and No.DMR-1629346X.Q.was supported by NSF Grant No.OAC-1835690A.A.acknowledges partial support by CHiMaD(NIST award#70NANB19H005)G.P.was supported by the Los Alamos National Laboratory’s Laboratory Directed Research and Development(LDRD)program’s Directed Research(DR)project#20200104DR。
文摘The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)techniques.JARVIS is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.
基金K.C.,K.F.G.,and F.T.thank National Institute of Standards and Technology for funding,computational,and data-management resourcesK.C.also thank the computational support from XSEDE computational resources under allocation number TG-DMR 190095VS acknowledges the XSEDE computational resources through allocation grant number TG-DMR160051 and the Advanced Computer Facility(ACF)of the University of Tennessee for computational resources.
文摘Many technological applications depend on the response of materials to electric fields,but available databases of such responses are limited.Here,we explore the infrared,piezoelectric,and dielectric properties of inorganic materials by combining highthroughput density functional perturbation theory and machine learning approaches.We computeΓ-point phonons,infrared intensities,Born-effective charges,piezoelectric,and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database.We find 3230 and 1943 materials with at least one far and mid-infrared mode,respectively.
