BAYESIAN

作品数:864被引量:1973H指数:16
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Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals
《npj Computational Materials》2024年第1期3024-3035,共12页A.Mahata T.Mukhopadhyay S.Chakraborty M.Asle Zaeem 
supported by the National Science Foundation,CMMI 2031800;The authors are grateful for the supercomputing time allocation provided by the NSF’s ACCESS(Advanced Cyberinfrastructure Coordination Ecosystem:Services&Support),Award No.DMR140008 and MAT210018.
Solidification phenomenon has been an integral part of the manufacturing processes of metals,where the quantification of stochastic variations and manufacturing uncertainties is critically important.Accurate molecular...
关键词:SOLIDIFICATION PROBABILISTIC ERROR 
Bayesian blacksmithing:discovering thermomechanical properties and deformation mechanisms in high-entropy refractory alloys
《npj Computational Materials》2024年第1期1545-1557,共13页Jacob Startt Megan J.McCarthy Mitchell A.Wood Sean Donegan Rémi Dingreville 
Finding alloys with specific design properties is challenging due to the large number of possible compositions and the complex interactions between elements.This study introduces a multiobjective Bayesian optimization...
关键词:deformation ALLOYS THERMOMECHANICAL 
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments
《npj Computational Materials》2024年第1期2946-2957,共12页Arpan Biswas Yongtao Liu Nicole Creange Yu-Chen Liu Stephen Jesse Jan-Chi Yang Sergei V.Kalinin Maxim A.Ziatdinov Rama K.Vasudevan 
supported by the Center for Nanophase Materials Sciences(CNMS),which is a US Department of Energy,Office of Science User Facility at Oak Ridge National Laboratory;supported by the US Department of Energy,Office of Science,Office of Basic Energy Sciences,MLExchange Project,award number 107514;supported by the center for 3D Ferroelectric Microelectronics(3DFeM),an Energy Frontier Research Center funded by the U.S.Department of Energy(DOE),Office of Science,Basic Energy Sciences under Award Number DE-SC0021118;the National Science and Technology Council(NSTC),Taiwan,under grant no.NSTC-111-2628-M-006-005.
Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade,with examples ranging from measurements of diffraction on combinatorial alloy...
关键词:structure system synthesis 
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 
Bayesian optimization acquisition functions for accelerated search of cluster expansion convex hull of multicomponent alloys
《npj Computational Materials》2024年第1期1035-1047,共13页Dongsheng Wen Victoria Tucker Michael S.Titus 
supported by the National Science Foundation under Grant No.(CAREER DMR-1848128);This research was supported in part through computational resources provided by Information Technology at Purdue,West Lafayette,Indiana^(40).D.W.would like to thank the University of Liverpool Library for Open Access Funds。
Atomistic simulations are crucial for predicting material properties and understanding phase stability,essential for materials selection and development.However,the high computational cost of density functional theory...
关键词:alloys OPTIMIZATION CLUSTER 
Autonomous atomic Hamiltonian construction and active sampling of X-ray absorption spectroscopy by adversarial Bayesian optimization
《npj Computational Materials》2023年第1期1890-1900,共11页Yixuan Zhang Ruiwen Xie Teng Long Damian Günzing Heiko Wende Katharina J.Ollefs Hongbin Zhang 
This work was also supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-Project-ID 405553726-TRR 270.We also acknowledge support by the Deutsche Forschungsgemeinschaft(DFG-German Research Foundation)and the Open Access Publishing Fund of Technical University of Darmstadt.
X-ray absorption spectroscopy(XAS)is a well-established method for in-depth characterization of electronic structure.In practice hundreds of energy-points should be sampled during the measurements,and most of them are...
关键词:structure HAMILTONIAN ABSORPTION 
Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC
《npj Computational Materials》2023年第1期2000-2007,共8页Yu Xie Jonathan Vandermause Senja Ramakers Nakib H.Protik Anders Johansson Boris Kozinsky 
YX was supported from the US Department of Energy(DOE),Office of Science,Office of Basic Energy Sciences(BES)under Award No.DE-SC0020128;JV was supported by the National Science Foundation award number 2003725;AJ was supported by the Aker scholarship.
Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.Active learning methods have been rec...
关键词:COMPUTER THERMAL utilize 
Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking
《npj Computational Materials》2023年第1期905-918,共14页Shuang Han Steen Lysgaard Tejs Vegge Heine Anton Hansen 
The authors thank the financial support from the BIKE project:BImetallic catalysts Knowledge-based development for Energy applications;The BIKE project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie Action-International Training Network(MSCA-ITN),grant agreement 813748;The authors also thank the Villum Fonden for funding through the project V-sustain(No.9455);the Niflheim Linux super-computer cluster installed at the Department of Physics at the Technical University of Denmark for providing computational resources.
Surface phase diagrams(SPDs)are essential for understanding the dependence of surface chemistry on reaction condition.For multi-component systems such as metal alloys,the derivation of such diagrams often relies on se...
关键词:ALLOY SURFACE DIAGRAMS 
Bayesian optimization with active learning of design constraints using an entropy-based approach被引量:2
《npj Computational Materials》2023年第1期1866-1879,共14页Danial Khatamsaz Brent Vela Prashant Singh Duane D.Johnson Douglas Allaire Raymundo Arróyave 
The authors acknowledge the support from the U.S.Department of Energy(DOE)ARPA-E ULTIMATE Program through Project DE-AR0001427 and DEVCOM-ARL under Contract No.W911NF2220106(HTMDEC);B.V.acknowledges the support of NSF through Grant No.DGE-1545403;D.K.acknowledges the support of NSF through Grant No.CDSE-2001333;R.A.acknowledges the support from Grants No.NSF-CISE-1835690 and NSF-DMREF-2119103.High-throughput CALPHAD and DFT calculations were carried out partly at the Texas A&M High-Performance Research Computing(HPRC)Facility.ARPA-E supported the applications of theory in this work.In contrast,the theory development(KKR-CPA and SCRAPs by DDJ/PS)at Ames National Laboratory were supported by the U.S.DOE,Office of Science,Basic Energy Sciences,Materials Science and Engineering Department.Ames Laboratory is operated by Iowa State University for the U.S.DOE under contract DE-AC02-07CH11358.
The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints.Candidate alloys must be ductile at room temperature and retain their yield stren...
关键词:ALLOYS SOLIDIFICATION ALLOY 
A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys
《npj Computational Materials》2023年第1期85-95,共11页Danial Khatamsaz Raymond Neuberger Arunabha M.Roy Sina Hossein Zadeh Richard Otis Raymundo Arróyave 
Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-22-2-0106;The views and conclu-sions contained in this document are those of the authors and should not be interpreted as representing the official policies,either expressed or implied,of the Army Research Laboratory or the U.S.Government.The U.S.Government is authorized to reproduce and distribute reprints for Government Purposes notwithstanding any copyright notation herein.Department of Energy(DOE)ARPA-E ULTIMATE Program through Project DE-AR0001427;DK acknowledges the support of NSF through Grant No.CDSE-2001333.RA acknowledges the support from Grants No.NSF-CISE-1835690 and NSF-DMREF-2119103;Part of this research was carried out at the Jet Propulsion Laboratory(JPL),California Institute of Technology,under a contract with the National Aeronautics and Space Administration(80NM0018D0004);This research was supported by the JPL Strategic University Research Partnership(SURP)program.
The design of materials and identification of optimal processing parameters constitute a complex and challenging task,necessitating efficient utilization of available data.Bayesian Optimization(BO)has gained popularit...
关键词:ALLOYS SHAPE APPROACH 
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