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Machine learning in materials informatics:recent applications and prospects

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npj Computational Materials 2017年第1期3卷 1-13页

作者： Rampi Ramprasad Rohit Batra ghanshyam pilania Arun Mannodi-Kanakkithodi Chiho Kim Department of Materials Science&Engineering and Institute of Materials Science University of Connecticut97 North Eagleville Rd.Unit 3136StorrsCT 06269-3136USA Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-614195 BerlinGermany Materials Science and Technology Division Los Alamos National LaboratoryLos AlamosNM 87545USA Center for Nanoscale Materials Lamont National Laboratory9700 S.Cass Ave.LemontIL 60439USA

Propelled partly by the Materials Genome Initiative,and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains,informatics strategies are beginning to take shape within materials *** approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly ***-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost,time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical *** are typically interpolative,involving fingerprinting a material numerically first,and then following a mapping(established via a learning algorithm)between the fingerprint and the property of ***,also referred to as“descriptors”,may be of many types and scales,as dictated by the application domain and *** may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into *** article attempts to provide an overview of some of the recent successful data-driven“materials informatics”strategies undertaken in the last decade,with particular emphasis on the fingerprint or descriptor *** review also identifies some challenges the community is facing and those that should be overcome in the near future.

关键词： overcome subset properly

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

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npj Computational Materials 2018年第1期4卷 254-261页

作者： Hongxiang Zong ghanshyam pilania Xiangdong Ding Graeme J.Ackland Turab Lookman State Key Laboratory for Mechanical Behavior of Materials Xi’an Jiaotong UniversityXi’an 710049China Theoretical Division&Materials Science and Technology Division Los Alamos National LaboratoryLos AlamosNM 87545USA Centre for Science at Extreme Conditions(CSEC) School of Physics and AstronomyUniversity of EdinburghEdinburgh EH93FDUK

Atomic simulations provide an effective means to understand the underlying physics of structural phase ***,this remains a challenge for certain allotropic metals due to the failure of classical interatomic potentials to represent the multitude of *** on machine-learning(ML)techniques,we develop a hybrid method in which interatomic potentials describing martensitic transformations can be learned with a high degree of fidelity from ab initio molecular dynamics simulations(AIMD).Using zirconium as a model system,for which an adequate semiempirical potential describing the phase transformation process is lacking,we demonstrate the feasibility and effectiveness of our ***,the ML-AIMD interatomic potential correctly captures the energetics and structural transformation properties of zirconium as compared to experimental and density-functional data for phonons,elastic constants,as well as stacking fault *** dynamics simulations successfully reproduce the transformation mechanisms and reasonably map out the pressure–temperature phase diagram of zirconium.

关键词： martensitic transformation zirconium

Anion order in oxysulfide perovskites:origins and implications

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npj Computational Materials 2020年第1期6卷 1062-1072页

作者： ghanshyam pilania Ayana Ghosh Steven T.Hartman Rohan Mishra Christopher R.Stanek Blas P.Uberuaga Materials Science and Technology Division Los Alamos National LaboratoryLos AlamosNM 87545USA Materials Science&Engineering University of ConnecticutStorrsCT 06269USA Institute of Materials Science&Engineering and Department of Mechanical Engineering&Materials Science Washington University in St.LouisSt.LouisMO 63130USA

Heteroanionic oxysulfide perovskite compounds represent an emerging class of new materials allowing for a wide range of tunability in the electronic structure that could lead to a diverse spectrum of novel and improved *** cation ordered double perovskites—where the origins and design rules of various experimentally observed cation orderings are well known and understood—anion ordering in heteroanionic perovskites remains a largely uncharted territory.

关键词： perovskite anionic ordered

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

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npj Computational Materials 2020年第1期6卷 234-246页

作者： Kamal Choudhary Kevin F.Garrity Andrew C.E.Reid Brian DeCost Adam J.Biacchi Angela R.Hight Walker Zachary Trautt Jason Hattrick-Simpers A.Gilad Kusne Andrea Centrone Albert Davydov Jie Jiang Ruth Pachter Gowoon Cheon Evan Reed Ankit Agrawal Xiaofeng Qian Vinit Sharma Houlong Zhuang Sergei V.Kalinin Bobby G.Sumpter ghanshyam pilania Pinar Acar Subhasish Mandal Kristjan Haule David Vanderbilt Karin Rabe Francesca Tavazza Materials Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Theiss Research La JollaCA 92037USA Department of Chemistry and Biochemistry University of MarylandCollege ParkMD 20742USA Physical Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Materials and Manufacturing Directorate Air Force Research LaboratoryWright–Patterson Air Force BaseDaytonOH 45433USA Department of Materials Science and Engineering Stanford UniversityStanfordCA 94305USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Department of Materials Science and Engineering Texas A&M UniversityTexasTX 77843USA Joint Institute for Computational Sciences University of TennesseeKnoxvilleTN 37996USA National Institute for Computational Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA School for Engineering of Matter Transport and Energy Arizona State UniversityTempeAZ 85287USA Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Materials Science and Technology Division Los Alamos National LabLos AlamosNM 87545USA Department of Mechanical Engineering Virginia TechBlacksburgVA 24061USA Department of Physics and Astronomy Rutgers UniversityPiscatawayNJ 08901USA

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)*** 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.

关键词： automated JAR databases