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Author Correction:Atomistic Line Graph Neural Network for improved materials property predictions

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npj Computational Materials 2022年第1期8卷 2117-2118页

作者： Kamal Choudhary brian decost Materials Measurement Laboratory National Institute of Standards and Technology Gaithersburg MD 20899 USA Theiss ResearchLa Jolla California CA 92037 USA DeepMaterials LLC Silver Spring MD 20906 USA

The original version of this Article contained errors in values of ALIGNN data in Table *** a result,the following changes have been made to the original version of this Article:In Table 5,the data for“OrbNetens5”co... 详细信息

关键词： property removed column

Atomistic Line Graph Neural Network for improved materials property predictions

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npj Computational Materials 2021年第1期7卷 1691-1698页

作者： Kamal Choudhary brian decost Materials Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Theiss ResearchLa Jolla California 92037USA DeepMaterials LLC Silver SpringMD 20906USA

Graph neural networks(GNN)have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning *** most existing GNN models for atomistic predictions are based on atomic distance information,they do not explicitly incorporate bond angles,which are critical for distinguishing many atomic ***,many material properties are known to be sensitive to slight changes in bond *** present an Atomistic Line Graph Neural Network(ALIGNN),a GNN architecture that performs message passing on both the interatomic bond graph and its line graph corresponding to bond *** demonstrate that angle information can be explicitly and efficiently included,leading to improved performance on multiple atomistic prediction *** ALIGNN models for predicting 52 solid-state and molecular properties available in the JARVIS-DFT,Materials project,and QM9 *** can outperform some previously reported GNN models on atomistic prediction tasks by up to 85%in accuracy with better or comparable model training speed.

关键词： explicitly property passing

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 *** allows analysis of unstructured data and automated identification of *** recent development of large materials databases has fueled the application of DL methods in atomistic prediction 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 *** 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 *** 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 *** 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

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks

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npj Computational Materials 2019年第1期5卷 624-632页

作者： Felipe Oviedo Zekun Ren Shijing Sun Charles Settens Zhe Liu Noor Titan Putri Hartono Savitha Ramasamy brian L.decost Siyu I.P.Tian Giuseppe Romano Aaron Gilad Kusne Tonio Buonassisi Massachusetts Institute of Technology CambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology Singapore 138602Singapore Institute for Infocomm Research(I2R) Agency for ScienceTechnology and Research(A*STAR)Singapore 138632Singapore National Institute of Standards and Technology MS 8520GaithersburgMD 20899USA

X-ray diffraction(XRD)data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film *** propose a machine learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD *** overcome the scarce data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model-agnostic,physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database(ICSD)and experimental *** a test case,115 thin-film metalhalides spanning three dimensionalities and seven space groups are synthesized and *** testing various algorithms,we develop and implement an all convolutional neural network,with cross-validated accuracies for dimensionality and space group classification of 93 and 89%,*** propose average class activation maps,computed from a global average pooling layer,to allow high model interpretability by human experimentalists,elucidating the root causes of ***,we systematically evaluate the maximum XRD pattern step size(data acquisition rate)before loss of predictive accuracy occurs,and determine it to be 0.16°2θ,which enables an XRD pattern to be obtained and classified in 5.5 min or less.

关键词： neural networks dimensionality

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