ATOMISTIC

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Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential
《npj Computational Materials》2023年第1期135-145,共11页Lei Zhang Gábor Csányi Erik van der Giessen Francesco Maresca 
This work made use of the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no.EINF-2393 and EINF-3104.We thank the Centre for Information Technology of the University of Groningen(UG)for their support and for providing access to the Peregrine high performance computing cluster.L.Z.would like to thank Predrag Andric for LAMMPS implementation of fracture simulation and Miguel Caro for the implementation of Turbo SOAP descriptor.F.M.acknowledges the support from the start-up grant from the Faculty of Science and Engineering at the University of Groningen.
The prediction of atomistic fracture mechanisms in body-centred cubic(bcc)iron is essential for understanding its semi-brittle nature.Existing atomistic simulations of the crack-tip under mode-I loading based on empir...
关键词：APPROXIMATION POTENTIAL apply 
Atomistic learning in the electronically grand-canonical ensemble被引量：1
《npj Computational Materials》2023年第1期1619-1627,共9页Xi Chen Muammar El Khatib Per Lindgren Adam Willard Andrew J.Medford Andrew A.Peterson 
The authors acknowledge support from the U.S.Department of Energy under Award DE-SC0019441;the National Science Foundation under award 1553365.Calculations were undertaken at Brown University’s Center for Computation and Visualization.
A strategy is presented for the machine-learning emulation of electronic structure calculations carried out in the electronically grand-canonical ensemble.The approach relies upon a dual-learning scheme,where both the...
关键词：CANONICAL ENSEMBLE ELECTRONIC 
Author Correction:Atomistic Line Graph Neural Network for improved materials property predictions被引量：4
《npj Computational Materials》2022年第1期2117-2118,共2页Kamal Choudhary Brian DeCost 
The original version of this Article contained errors in values of ALIGNN data in Table 5.As a result,the following changes have been made to the original version of this Article:In Table 5,the data for“OrbNetens5”c...
关键词：PROPERTY removed COLUMN 
An atomistic approach for the structural and electronic properties of twisted bilayer graphene-boron nitride heterostructures
《npj Computational Materials》2022年第1期687-696,共10页Min Long Pierre A.Pantaleón Zhen Zhan Francisco Guinea JoseÁngel Silva-Guillén Shengjun Yuan 
This work is supported by the National Science Foundation of China(Grant No.11774269 and No.12047543);IMDEA Nanociencia acknowledges support from the“Severo Ochoa"Programme for Centres of Excellence in R&D(Grant No.SEV-2016-0686);P.A.P and F.G.acknowledge funding from the European Commission,within the Graphene Flagship,Core 3,grant number 881603;the grant NMAT2D(Comunidad de Madrid,Spain);S.Y.acknowledges funding from the National Key R&D Program of China(Grant No.2018YFA0305800).
Twisted bilayer graphene(TBG)has taken the spotlight in the condensed matter community since the discovery of correlated phases.In this work,we study heterostructures of TBG and hexagonal boron nitride(hBN)using an at...
关键词：BORON ELECTRONIC TWISTED 
Atomistic Line Graph Neural Network for improved materials property predictions被引量：21
《npj Computational Materials》2021年第1期1691-1698,共8页Kamal Choudhary Brian DeCost 
K.C.and B.D.thank the National Institute of Standards and Technology for funding,computational,and data management resources.Contributions from K.C.were supported by the financial assistance award 70NANB19H117 from the U.S.Department of Commerce,National Institute of Standards and Technology.This work was also supported by the Frontera supercomputer,National Science Foundation OAC-1818253;at the Texas Advanced Computing Center(TACC)at The University of Texas at Austin.
Graph neural networks(GNN)have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.While most existing G...
关键词：explicitly PROPERTY PASSING 
Microstructural impacts on ionic conductivity of oxide solid electrolytes from a combined atomistic-mesoscale approach
《npj Computational Materials》2021年第1期1959-1973,共15页Tae Wook Heo Andrew Grieder Bo Wang Marissa Wood Tim Hsu Sneha A.Akhade Liwen F.Wan Long-Qing Chen Nicole Adelstein Brandon C.Wood 
This work was performed under the auspices of the U.S.Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344;The authors acknowledge financial support from the U.S.Department of Energy(DOE),Office of Energy Efficiency and Renewable Energy,Vehicle Technologies Office,through the Battery Materials Research program.This work was partially funded by the Laboratory Directed Research and Development Program at LLNL under the project tracking code 15-ERD-022 and 18-FS-019.Additional computing support came from the LLNL Institutional Computing Grand Challenge program.The work of A.Grieder and N.Adelstein was supported by the National Science Foundation under Grant No.DMR-1710630 and simulations utilized the Extreme Science and Engineering Discovery Environment(XSEDE)83 Stampede2 at the University of Texas,Austin through allocation DMR180033.Work at The Pennsylvania State University is partially supported by the Donald W.Hamer Foundation through a Hamer Professorship.Helpful discussions about experimental microstructures of solid electrolytes with J.Ye(LLNL)are acknowledged.
Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged,practical processing and synthesis routes introduce grain boundaries and other interfaces that can pe...
关键词：microstructure solid CONDUCTIVITY 
Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data被引量：2
《npj Computational Materials》2020年第1期570-578,共9页Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang Rama K.Vasudevan Eugene Eliseev Anna N.Morozovska Ichiro Takeuchi Sergei V.Kalinin 
The work at the University of Maryland was supported in part by the National Institute of Standards and Technology Cooperative Agreement 70NANB17H301;the Center for Spintronic Materials in Advanced infoRmation Technologies(SMART)one of centers in nCORE,a Semiconductor Research Corporation(SRC)program sponsored by NSF and NIST;A.N.M.work was partially supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie(grant agreement No 778070).
Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extra...
关键词：FERROELECTRIC analysis FOUNDING 
On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events被引量：22
《npj Computational Materials》2020年第1期1502-1512,共11页Jonathan Vandermause Steven B.Torrisi Simon Batzner Yu Xie Lixin Sun Alexie M.Kolpak Boris Kozinsky 
B.K.acknowledges generous gift funding support from Bosch Research and partial support from the National Science Foundation under Grant No.1808162;L.S.was supported by the Integrated Mesoscale Architectures for Sustainable Catalysis(IMASC),an Energy Frontier Research Center funded by the U.S.Department of Energy,Office of Science,Basic Energy Sciences under Award#DE-SC0012573;A.M.K.and S.B.acknowledge funding from the MIT-Skoltech Center for Electrochemical Energy Storage.S.B.T.is supported by the Department of Energy Computational Science Graduate Fellowship under grant DE-FG02-97ER25308.
Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations,which can result in low training efficiency and unpredictable errors when ap...
关键词：FIELDS ELEMENT typically 
Bayesian inference of atomistic structure in functional materials被引量：2
《npj Computational Materials》2019年第1期836-842,共7页Milica Todorović Michael U.Gutmann Jukka Corander Patrick Rinke 
This work was supported by the Academy of Finland through Project Nos.251748,284621 and 316601,and also through the European Union’s Horizon 2020 research and innovation programme under Grant agreement No.676580;The Novel Materials Discovery(NOMAD)Laboratory,a European Center of Excellence.J.C.was funded by the ERC grant no.742158.
Tailoring the functional properties of advanced organic/inorganic heterogeneous devices to their intended technological applications requires knowledge and control of the microscopic structure inside the device.Atomis...
关键词：ADSORPTION FUNCTIONAL INORGANIC 
Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy:an atomistic simulation study被引量：16
《npj Computational Materials》2018年第1期662-670,共9页Won-Mi Choi Yong Hee Jo Seok Su Sohn Sunghak Lee Byeong-Joo Lee 
This research was supported by the Future Material Discovery Program of the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT of Korea(2016M3D1A1023383).
Although high-entropy alloys(HEAs)are attracting interest,the physical metallurgical mechanisms related to their properties have mostly not been clarified,and this limits wider industrial applications,in addition to t...
关键词：ALLOY ALLOYING ENTROPY 
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