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PROPERTY: 作品数：2159被引量：3516H指数：20; 导出分析报告; 相关领域：理学更多>>; 相关作者：叶耀军刘仲奎朱质彬梁辉郝俊杰更多>>; 相关机构：清华大学中国科学院天津大学北京理工大学更多>>; 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国家高技术研究发展计划更多>>

Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning: 《npj Computational Materials》2024年第1期3135-3148,共14页Hajime Shimakawa Akiko Kumada Masahiro Sato; Data-driven materials science has realized a new paradigm by integrating materials domain knowledge and machine-learning(ML)techniques.However,ML-based research has often overlooked the inherent limitation in predicti...; 关键词：PROPERTY QUANTUM PREDICTION

On-demand reverse design of polymers with PolyTAO: 《npj Computational Materials》2024年第1期317-327,共11页Haoke Qiu Zhao-Yan Sun; the National Key R&D Program of China(No.2022YFB3707303);the National Natural Science Foundation of China(No.52293471);supported by the hardware in the Network and Computing Center at Changchun Institute of Applied Chemistry,Chinese Academy of Sciences.; The forward screening and reverse design of drug molecules,inorganic molecules,and polymers with enhanced properties are vital for accelerating the transition from laboratory research to market application.Specificall...; 关键词：PROPERTY thereby INORGANIC

Structure-based out-of-distribution(OOD)materials property prediction:a benchmark study: 《npj Computational Materials》2024年第1期1753-1766,共14页Sadman Sadeed Omee Nihang Fu Rongzhi Dong Ming Hu Jianjun Hu; supported in part by National Science Foundation under the grants 2110033,OAC-2311203,and 2320292.; In real-world materials research,machine learning(ML)models are usually expected to predict and discover novel exceptional materials that deviate from the known materials.It is thus a pressing question to provide an o...; 关键词：PROPERTY PREDICTION DISTRIBUTION

Property-guided generation of complex polymer topologies using variational autoencoders: 《npj Computational Materials》2024年第1期1825-1837,共13页Shengli Jiang Adji Bousso Dieng Michael A.Webb; M.A.W.and A.B.D acknowledge funding from the Princeton Catalysis Initiative for this research;M.A.W.and S.J.also acknowledge support from the donors of ACS Petroleum Research Fund under Doctoral New Investigator Grant 66706-DNI7.; The complexity and diversity of polymer topologies,or chain architectures,present substantial challenges in predicting and engineering polymer properties.Although machine learning is increasingly used in polymer scien...; 关键词：properties VARIATIONAL POLYMER

Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis: 《npj Computational Materials》2024年第1期628-637,共10页Hoje Chun Jaclyn R.Lunger JeungKu Kang Rafael Gómez-Bombarelli Byungchan Han; H.C.,J.K.K.and B.H.acknowledge National Research Foundation of Korea(2022M3H4A1A04096482 and RS-2023-00229679)funded by the Ministry of Science and ICT.H.C.also acknowledges“Program for Fostering Innovative Global Leaders”of the Korea Institute for Advancement of Technology(KIAT)with financial support by the Ministry of Trade,Industry&Energy(MOTIE),Republic of Korea(P0017304);supported by the Under Secretary of Defense for Research and Engineering under Air Force Contract No.FA8702-15-D-0001。; Single-atom catalysts(SACs)with multiple active sites exhibit high activity for a wide range of sluggish reactions,but identifying optimal multimetallic SAC is challenging due to the vast design space.Here,we present ...; 关键词：space METALLIC PROPERTY

Mapping structure-property relationships in fullerene systems:a computational study from C_(20)to C_(60): 《npj Computational Materials》2024年第1期833-843,共11页Bin Liu Jirui Jin Mingjie Liu; supported by the University of Florida’s new faculty start-up funding.; Fullerenes,as characteristic carbon nanomaterials,offer significant potential for diverse applications due to their structural diversity and tunable properties.Numerous isomers can exist for a specific fullerene size,...; 关键词：PROPERTIES PROPERTY ISOMERS

Chemical-motif characterization of short-range order with E(3)-equivariant graph neural networks: 《npj Computational Materials》2024年第1期983-992,共10页Killian Sheriff Yifan Cao Rodrigo Freitas; supported by the MathWorks Ignition Fund,MathWorks Engineering Fellowship Fund,and the Portuguese Foundation for International Cooperation in Science,Technology,and Higher Education in the MIT-Portugal Program;supported by the Research Support Committee Funds from the School of Engineering at the Massachusetts Institute of Technology;This work used the Expanse supercomputer at the San Diego Supercomputer Center through allocation MAT210005 from the Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support(ACCESS)program,which is supported by National Science Foundation grants#2138259,#2138286,#2138307,#2137603,and#2138296;the Extreme Science and Engineering Discovery Environment(XSEDE),which was supported by National Science Foundation grant number#1548562.; Crystalline materials have atomic-scale fluctuations in their chemical composition that modulate various mesoscale properties.Establishing chemistry–microstructure relationships in such materials requires proper char...; 关键词：properties CHARACTERIZATION PROPERTY

MD-HIT:Machine learning for material property prediction with dataset redundancy control: 《npj Computational Materials》2024年第1期638-648,共11页Qin Li Nihang Fu Sadman Sadeed Omee Jianjun Hu; supported in part by National Science Foundation under the grant number 2311202.; Materials datasets usually contain many redundant(highly similar)materials due to the tinkering approach historically used in material design.This redundancy skews the performance evaluation of machine learning(ML)mod...; 关键词：PREDICTION PROPERTY performance

DenseGNN:universal and scalable deeper graph neural networks for highperformance property prediction in crystals and molecules: 《npj Computational Materials》2024年第1期95-110,共16页Hongwei Du Jiamin Wang Jian Hui Lanting Zhang Hong Wang; Weare grateful for the financial support fromthe National Key Research and Development Program of China(Grant Nos.2021YFB3702104).; Moderngenerative modelsbasedondeep learning havemadeit possible to design millions of hypothetical materials.To screen these candidate materials and identify promising new materials,we need fast and accuratemodels to ...; 关键词：properties materials PROPERTY

TransPolymer: a Transformer-based language model for polymer property predictions被引量：4: 《npj Computational Materials》2023年第1期1703-1716,共14页Changwen Xu Yuyang Wang Amir Barati Farimani; Accurate and efficient prediction of polymer properties is of great significance in polymer design.Conventionally,expensive and time-consuming experiments or simulations are required to evaluate polymer functions.Rece...; 关键词：PROPERTY POLYMER RATIONAL

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