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Prediction of Weyl semimetal and antiferromagnetic topological insulator phases in Bi_(2)MnSe_(4)

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npj Computational Materials 2019年第1期5卷 867-873页

作者： Sugata Chowdhury Kevin F.Garrity francesca tavazza Materials Measurement Laboratory National Institute for Standards and TechnologyGaithersburgMD 20899USA

Three-dimensional materials with strong spin–orbit coupling and magnetic interactions represent an opportunity to realize a variety of rare and potentially useful topological phases with broken time-reversal symmetry.In this work,we use first principles calculations to show that the recently synthesized material Bi_(2)MnSe_(4) displays a combination of spin–orbit-induced band inversion,also observed in non-magnetic topological insulator Bi2PbSe4,with magnetic interactions,leading to several topological phases.In bulk form,the ferromagnetic phase of Bi_(2)MnSe_(4) has symmetry protected band crossings at the Fermi level,leading to either a nodal line or Weyl semimetal,depending on the direction of the spins.Due to the combination of time reversal symmetry plus a partial translation,the ground state layered antiferromagnetic phase is instead an antiferromagnetic topological insulator.The surface of this phase intrinsically breaks time-reversal symmetry,allowing the observation of the half-integer quantum anomalous Hall effect.Furthermore,we show that in thin film form,for sufficiently thick slabs,Bi_(2)MnSe_(4) becomes a Chern insulator with a band gap of up to 58 meV.This combination of properties in a stoichiometric magnetic material makes Bi_(2)MnSe_(4) an excellent candidate for displaying robust topological behavior.

关键词： topological insulator symmetry

Computational search for magnetic and non-magnetic 2D topological materials using unified spin-orbit spillage screening

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npj Computational Materials 2020年第1期6卷 1272-1279页

作者： Kamal Choudhary Kevin F.Garrity Jie Jiang Ruth Pachter francesca tavazza Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD 20899USA Materials Directorate Air Force Research LaboratoryWright–Patterson Air Force BaseOH 45433USA

Two-dimensional topological materials(2D TMs)have a variety of properties that make them attractive for applications including spintronics and quantum computation.However,there are only a few such experimentally known materials.To help discover new 2D TMs,we develop a unified and computationally inexpensive approach to identify magnetic and non-magnetic 2D TMs,including gapped and semi-metallic topological classifications,in a high-throughput way using density functional theory-based spin–orbit spillage,Wannier-interpolation,and related techniques.

关键词： materials unified topological

High-throughput density functional perturbation theory and machine learning predictions of infrared,piezoelectric,and dielectric responses

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npj Computational Materials 2020年第1期6卷 1119-1131页

作者： Kamal Choudhary Kevin F.Garrity Vinit Sharma Adam J.Biacchi Angela R.Hight Walker francesca tavazza Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD 20899USA National Institute for Computational Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Joint Institute for Computational Sciences University of TennesseeKnoxvilleTN 37996USA Engineering Physics Division National Institute of Standards and TechnologyGaithersburgMD 20899USA

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.

关键词： piezoelectric dielectric perturbation

Recent advances and applications of deep learning methods in materials science

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npj Computational Materials 2022年第1期8卷 548-573页

作者： Kamal Choudhary Brian DeCost Chi Chen Anubhav Jain francesca tavazza Ryan Cohn Cheol Woo Park Alok Choudhary Ankit Agrawal Simon J.L.Billinge Elizabeth Holm Shyue Ping Ong Chris Wolverton Materials Science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA Theiss Research La JollaCA92037USA DeepMaterials LLC Silver SpringMD20906USA Material Measurement Science Division National Institute of Standards and TechnologyGaithersburgMD20899USA Department of NanoEngineering University of California San DiegoSan DiegoCA92093USA Energy Technologies Area Lawrence Berkeley National LaboratoryBerkeleyCAUSA Department of Materials Science and Engineering Carnegie Mellon UniversityPittsburghPA15213USA Department of Materials Science and Engineering Northwestern UniversityEvanstonIL60208USA Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL60208USA Department of Applied Physics and Applied Mathematics and the Data Science Institute Fu Foundation School of Engineering and Applied SciencesColumbia UniversityNew YorkNY10027USA

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

关键词： learning limitations textual

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)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.

关键词： automated JAR databases