云南高校图书馆联盟文献共享服务平台- CRYSTALLINE

CRYSTALLINE: 作品数：662被引量：815H指数：10; 导出分析报告; 相关领域：理学更多>>; 相关作者：董志刚黄巨富王耀萍吕玉兵汪道涌更多>>; 相关机构：施华洛世奇股份公司中国科学院中国科学院植物研究所四川大学更多>>; 相关期刊：更多>>; 相关基金：国家自然科学基金国家重点基础研究发展计划中国博士后科学基金高等学校学科创新引智计划更多>>

Superconductivity of metastable dihydrides at ambient pressure: 《npj Computational Materials》2024年第1期1450-1455,共6页Heejung Kim Ina Park J.H.Shim D.Y.Kim; supported by the National Research Foundation(NRF)Korea(Grant No.2020R1A5A1019141,2021R1C1C2011276,RS-2023-00220471,RS-2023-00272090,RS-2024-00406755);D.Y.K.acknowledges the support from the National Natural Science Foundation of China(11774015)。; Hydrogen in metals is a significant research area with far-reaching implications,encompassing diverse fields such as hydrogen storage,metal-insulator transitions,and the recently emerging phenomenon of room-temperatur...; 关键词：hydrides METASTABLE CRYSTALLINE

Towards end-to-end structure determination from x-ray diffraction data using deep learning: 《npj Computational Materials》2024年第1期1048-1059,共12页Gabe Guo Judah Goldfeder Ling Lan Aniv Ray Albert Hanming Yang Boyuan Chen Simon J.L.Billinge Hod Lipson; supported by U.S.National Science Foundation under AI Institute for Dynamical Systems grant 2112085;supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences(DOE-BES)undercontract No.DESC0024141.; Powder crystallography is the experimental science of determining the structure of molecules provided in crystalline-powder form,by analyzing their x-ray diffraction(XRD)patterns.Since many materials are readily avail...; 关键词：STRUCTURE POWDER CRYSTALLINE

Shotgun crystal structure prediction using machine-learned formation energies: 《npj Computational Materials》2024年第1期13-26,共14页Liu Chang Hiromasa Tamaki Tomoyasu Yokoyama Kensuke Wakasugi Satoshi Yotsuhashi Minoru Kusaba Artem R.Oganov Ryo Yoshida; supported in part by a Ministry of Education,Culture,Sports,Science and Technology(MEXT)KAKENHI Grant-in-Aid for Scientific Research on Innovative Areas(grant number 19H05820);Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(A)(grant number 19H01132);Early-Career Scientists(grant number 23K16955);JST CREST(grant numbers JPMJCR19I3,JPMJCR22O3,and JPMJCR2332);Computational resources were partly provided by the supercomputer at the Research Center for Computational Science,Okazaki,Japan(projects 23-IMSC113 and 24-IMS-C107).; Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations.Generally,this requires repeated fi...; 关键词：CRYSTAL STRUCTURE CRYSTALLINE

The ab initio non-crystalline structure database:empowering machine learning to decode diffusivity: 《npj Computational Materials》2024年第1期151-161,共11页Hui Zheng Eric Sivonxay Rasmus Christensen Max Gallant Ziyao Luo Matthew McDermott Patrick Huck Morten M.Smedskjær Kristin A.Persson; This research was intellectually led by the Materials Project program(Contract No.DE-AC02-05-CH11231,KC23MP);supported by the US Department of Energy,Office of Basic Energy Sciences.; Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology,ranging from optical and electronic devices and solid-state batteries to protective coatin...; 关键词：CRYSTALLINE DATABASE structure

Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions: 《npj Computational Materials》2023年第1期733-743,共11页Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein William Cai Evan J.Reed; This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.We would like to thank Prof.Tony Heinz for the original project inspiration and the human participants of the Synthesizability Quiz.; Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials discovery.In this work,we develop a deep learning synthesizability model(SynthNN)...; 关键词：INORGANIC CRYSTALLINE utilize

Robust combined modeling of crystalline and amorphous silicon grain boundary conductance by machine learning: 《npj Computational Materials》2022年第1期2099-2106,共8页Chayaphol Lortaraprasert Junichiro Shiomi; This work was partially supported by JST-CREST(Grant No.JPMJCR21O2).; Knowledge in thermal and electric transport through grain boundary(GB)is crucial for designing nanostructured thermoelectric materials,where the transport greatly depends on GB atomistic structure.In this work,we empl...; 关键词：GRAIN structure. CRYSTALLINE

Disentangling multiple scattering with deep learning:application to strain mapping from electron diffraction patterns被引量：2: 《npj Computational Materials》2022年第1期2419-2433,共15页Joydeep Munshi Alexander Rakowski Benjamin H.Savitzky Steven E.Zeltmann Jim Ciston Matthew Henderson Shreyas Cholia Andrew M.Minor Maria K.Y.Chan Colin Ophus; This work was primarily funded by the US Department of Energy in the program“4D Camera Distillery:From Massive Electron Microscopy Scattering Data to Useful Information with AI/ML.”;M.K.Y.C.and C.O.each acknowledge support of a US Department of Energy Early Career Research Award;J.C.acknowledges support from the Presidential Early Career Award for Scientists and Engineers(PECASE)through the U.S.Department of Energy.B.H.S.and py4DSTEM development are supported by the Toyota Research Institute;S.E.Z.was supported by the National Science Foundation under STROBE Grant no.DMR 1548924;Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the US Department of Energy under Contract No.DE-AC02-05CH11231;Use of the Center for Nanoscale Materials,an Office of Science user facility,was supported by the U.S.Department of Energy,Office of Science,Office of Basic Energy Sciences,under Contract No.DE-AC02-06CH11357;This research used resources of the National Energy Research Scientific Computing Center,a DOE Office of Science User Facility supported by the Office of Science of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231。; A fast,robust pipeline for strain mapping of crystalline materials is important for many technological applications.Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial r...; 关键词：VALUED adapted CRYSTALLINE

Crystal twins:self-supervised learning for crystalline material property prediction被引量：3: 《npj Computational Materials》2022年第1期2210-2217,共8页Rishikesh Magar Yuyang Wang Amir Barati Farimani; The work has been supported by the start-up fund provided by Department of Mechanical Engineering at Carnegie Mellon University.; Machine learning(ML)models have been widely successful in the prediction of material properties.However,large labeled datasets required for training accurate ML models are elusive and computationally expensive to gene...; 关键词：PREDICTION PROPERTY CRYSTALLINE

CrysXPP:An explainable property predictor for crystalline materials被引量：2: 《npj Computational Materials》2022年第1期424-434,共11页Kishalay Das Bidisha Samanta Pawan Goyal Seung-Cheol Lee Satadeep Bhattacharjee Niloy Ganguly; We present a deep-learning framework,CrysXPP,to allow rapid and accurate prediction of electronic,magnetic,and elastic properties of a wide range of materials.CrysXPP lowers the need for large property tagged datasets...; 关键词：materials. PROPERTY CRYSTALLINE

Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks被引量：3: 《npj Computational Materials》2022年第1期1886-1898,共13页Zhenze Yang Markus J.Buehler; We acknowledge support from the Army Research Office(W911NF1920098);AFOSR-MURI(FA9550-15-1-0514).; Structural defects are abundant in solids,and vital to the macroscopic materials properties.However,a defect-property linkage typically requires significant efforts from experiments or simulations,and often contains l...; 关键词：structural SOLIDS CRYSTALLINE

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