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An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems

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npj Computational Materials 2023年第1期9卷 1217-1226页

作者： Yuwei Mao Mahmudul Hasan Arindam Paul Vishu Gupta Kamal Choudhary Francesca Tavazza Wei-keng Liao Alok Choudhary Pinar Acar ankit agrawal Department of Electrical and Computer Engineering Northwestern UniversityEvanstonILUSA Virginia Tech BlacksburgVAUSA Materials Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD 20899USA Theiss Research La JollaCA 92037USA

Materials design aims to identify the material features that provide optimal properties for various engineering applications,such as aerospace,automotive,and *** of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal *** paper proposes an end-to-end artificial intelligence(AI)-driven microstructure optimization framework for elastic properties of *** this work,the microstructure is represented by the Orientation Distribution Function(ODF)that determines the volume densities of crystallographic *** framework was evaluated on two crystal systems,cubic and hexagonal,for Titanium(Ti)in Joint Automated Repository for Various Integrated Simulations(JARVIS)database and is expected to be widely applicable for materials with multiple crystal *** proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.

关键词： microstructure cubic crystal

A comprehensive review on pharmacognosy, phytochemistry and pharmacological activities of 8 potent Prunus species of southeast Asia

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Journal of Traditional Chinese Medicine 2024年第3期44卷 620-628页

作者： Shikha agrawal Adarsh Kumar ankit Kumar Singh Harshwardhan Singh Suresh Thareja Pradeep Kumar Department of Pharmaceutical Sciences and Natural Products Central University of PunjabBathinda 151401India

Genus Prunus comprising around 430 species is a vast important genus of family Rosaceae, subfamily amygdalaoidae. Among all 430 species, around 19 important species are commonly found in Indian subcontinent due to their broad nutritional and economic importance. Some most common species of genus Prunus are Prunus amygdalus, Prunus persica, Prunus armeniaca, Prunus avium, Prunus cerasus, Prunus cerasoides, Prunus domestica, Prunus mahaleb, etc. A newly introduced species of Prunus i.e Prunus sunhangii is recently discovered which is morphologically very similar to Prunus cerasoides. Plants of Prunus species are short to medium-sized deciduous trees mainly found in the northern hemisphere. In India and its subcontinent,it extends from the Himalayas to Sikkim, Meghalaya,Bhutan, Myanmar etc. Different Prunus species have been extensively studied for their morphological,microscopic, pharmacological and phytoconstituents characteristics. Total phenolic content of Prunus species explains the presence of phenols in high quantity and pharmacological activity due to phenols. Phytochemical screening of species of genus Prunus shows the presence of wide phytoconstituents which contributes in their pharmacological significance and reveals the therapeutic potential and traditional medicinal significance of this genus. Genus Prunus showed a potent antioxidant activity analyzed by 1,1-diphenyl-2-picryl-hydrazyl radical assay. Plant species belonging to the genus Prunus is widely used traditionally for the treatment of various disorders. Some specific Prunus species possess potent anticancer, anti-inflammatory,hypoglycemic etc. activity which makes the genus more interesting for further research and findings. This review is an attempt to summarize the comprehensive study of Prunus species from its distribution, morphological characters to phytoconstituents, and pharmacological activity.

关键词： Rosaceae Prunus antineoplastic agents hypoglycemic agents distribution phytochemistry review

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

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