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Physics guided heat source for quantitative prediction of IN718 laser additive manufacturing processes
《npj Computational Materials》2024年第1期2854-2867,共14页Abdullah Al Amin Yangfan Li Ye Lu Xiaoyu Xie Zhengtao Gan Satyajit Mojumder Gregory J.Wagner Wing Kam Liu 
the NIST AM Bench organizing committee for their high-quality experimental datasets and the United States National Science Foundation(NSF)funding under Grant No.CMMI-1934367 and Center for Hierarchical Material Design(CHiMaD).
Challenge 3 of the 2022 NIST additive manufacturing benchmark(AM Bench)experiments asked modelers to submit predictions for solid cooling rate,liquid cooling rate,time above melt,and melt pool geometry for single and ...
关键词：prediction SOURCE ADDITIVE 
Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs
《npj Computational Materials》2024年第1期2171-2181,共11页Andre K.Y.Low Flore Mekki-Berrada Abhishek Gupta Aleksandr Ostudin Jiaxun Xie Eleonore Vissol-Gaudin Yee-Fun Lim Qianxiao Li Yew Soon Ong Saif A.Khan Kedar Hippalgaonkar 
funding from AME Programmatic Funds by the Agency for Science,Technology and Research under Grant No.A1898b0043 and No.A20G9b0135;KH also acknowledges funding from the National Research Foundation(NRF),Singapore under the NRF Fellowship(NRF-NRFF13-2021-0011);SAK and FMB also acknowledge funding from the 25th NRF CRP programme(NRF-CRP25-2020RS-0002);QL also acknowledges support from the NRF fellowship(project No.NRF-NRFF13-2021-0005);the Ministry of Education,Singapore,under its Research Centre of Excellence award to the Institute for Functional Intelligent Materials(I-FIM,project No.EDUNC-33-18-279-V12).
The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces.To reach target properties efficiently,these platforms are increasingly paired with int...
关键词：OPTIMIZATION driving CONSTRAINED 
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 
Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints被引量：6
《npj Computational Materials》2023年第1期1275-1275,共1页Yong Zhao Edirisuriya M.Dilanga Siriwardane Zhenyao Wu Nihang Fu Mohammed Al-Fahdi Ming Hu Jianjun Hu 
Correction to:npj Computational Materials https://doi.org/10.1038/s41524-023-00987-9,published online 18 March 2023 The original version of this Article contained typos in both the PDF and the HTML versions.In the fou...
关键词：HTML constraints SYMMETRY 
Machine learning guided high-throughput search of non-oxide garnets
《npj Computational Materials》2023年第1期1717-1725,共9页Jonathan Schmidt Hai-Chen Wang Georg Schmidt Miguel A.L.Marques 
Garnets have found important applications in modern technologies including magnetorestriction,spintronics,lithium batteries,etc.The overwhelming majority of experimentally known garnets are oxides,while explorations(e...
关键词：structure SULFIDE OXIDES 
Physics guided deep learning for generative design of crystal materials with symmetry constraints被引量：1
《npj Computational Materials》2023年第1期1969-1980,共12页Yong Zhao Edirisuriya M.Dilanga Siriwardane Zhenyao Wu Nihang Fu Mohammed Al-Fahdi Ming Hu Jianjun Hu 
The research reported in this work was supported in part by National Science Foundation under the grant and 2110033,1940099 and 1905775.The views,perspectives,and content do not necessarily represent the official views of the NSF.
Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventionalapproaches based on experiments and simulations are labor-intensive or costly with success hea...
关键词：SYMMETRY CRYSTAL MATERIALS 
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification被引量：2
《npj Computational Materials》2023年第1期2036-2043,共8页Nathan J.Szymanski Christopher J.Bartel Yan Zeng Mouhamad Diallo Haegyeom Kim Gerbrand Ceder 
This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231;We also acknowledge support from the U.S.Department of Energy,Office of Science,Basic Energy Sciences,under Contract No.DE-AC02-05-CH11231 within the Joint Center for Energy Storage Research(JCESR)program;Computing was performed using resources from the Center for Functional Nanomaterials(CFN),which is a U.S.DOE Office of Science User Facility,at Brookhaven National Laboratory under Contract No.DE-SC0012704;N.J.S.was supported in part by the National Science Foundation Graduate Research Fellowship under grant#1752814.
Machine learning(ML)has become a valuable tool to assist and improve materials characterization,enabling automated interpretation of experimental results with techniques such as X-ray diffraction(XRD)and electron micr...
关键词：phase DIFFRACTION AUTONOMOUS 
Understanding and design of metallic alloys guided by phase-field simulations被引量：10
《npj Computational Materials》2023年第1期1377-1401,共25页Yuhong Zhao 
Also supported by National Natural Science Foundation of China(Nos.52074246,52201146,52205429,52275390);National Defense Basic Scientific Research Program of China(No.JCKY2020408B002);Key Research and Development Program of Shanxi Province(202102050201011).Many thanks to Dr.XL Tian of North University of China for her kind effort and time in checking,processing,and editing,and Professor L.Q.Chen of Pennsylvania State University for his invitation and critical feedback.
Phase-field method(PFM)has become a mainstream computational method for predicting the evolution of nano and mesoscopic microstructures and properties during materials processes.The paper briefly reviews latest progre...
关键词：ALLOYS MICROSTRUCTURE METALLIC 
Machine learning guided discovery of ternary compounds involving La and immiscible Co and Pb elements
《npj Computational Materials》2022年第1期2462-2470,共9页Renhai Wang Weiyi Xia Tyler JSlade Xinyu Fan Huafeng Dong Kai-Ming Ho Paul C.Canfield Cai-Zhuang Wang 
Work at Ames Laboratory was supported by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences,Materials Science and Engineering Division including a grant of computer time at the National Energy Research Scientific Computing Centre(NERSC)in Berkeley,CA.Ames Laboratory is operated for the U.S.DOE by Iowa State University under Contract No.DE-AC02-07CH11358;Work at Guangdong University of Technology was supported by the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515110328&2022A1515012174);the Guangdong Natural Science Foundation of China(Grant No.2019B1515120078);R.H.Wang and H.F.Dong also thank Center of Campus Network&Modern Educational Technology,Guangdong University of Technology,Guangdong,China for providing computational resources and technical support for this work.T.J.S was supported by the U.S.Department of Energy,Office of Basic Energy Sciences,through Ames Laboratory under its Contract with Iowa State University(No.DE-AC02-07CH11358)and through the Center for Advancement of Topological Semimetals(CATS);T.J.S.was also supported by the Gordon and Betty Moore Foundation(Grant No.GBMF4411).
Ternary compounds with an immiscible pair of elements are relatively unexplored but promising for novel quantum materials discovery.Exploring what third element and its ratio that can be added to make stable ternary c...
关键词：ELEMENTS IMMISCIBLE TERNARY 
Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning被引量：5
《npj Computational Materials》2021年第1期1242-1251,共10页Z.Q.Zhou Q.F.He X.D.Liu Q.Wang J.H.Luan C.T.Liu Y.Yang 
The research of YY is supported by the Research Grant Council,the Hong Kong Government,through the General Research Fund(GRF)with the grant numbers CityU11209317,CityU11213118,and CityU11200719;Atom probe tomography research was conducted by Dr.JH LUAN at the Inter-University 3D Atom Probe Tomography Unit of City University of Hong Kong,which is supported by the CityU grant 9360161。
The compositional design of metallic glasses(MGs)is a long-standing issue in materials science and engineering.However,traditional experimental approaches based on empirical rules are time consuming with a low efficie...
关键词：ALLOY GLASSES METALLIC 
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