Gus L. W. Hart

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Curtarolo, StefanoDuke UniversityEmail yang diverifikasi di nietzsche.mems.duke.edu
Lance J. NelsonBrigham Young University - IdahoEmail yang diverifikasi di byui.edu
Alex ZungerUniversity of Colorado ,Boulder,Colorado.Renewable and Sustainable Energy InstituteEmail yang diverifikasi di colorado.edu
Michael MehlDuke UniversityEmail yang diverifikasi di duke.edu
Conrad RosenbrockBrigham Young UniversityEmail yang diverifikasi di byu.edu
Marco Buongiorno NardelliRegents Professor of Physics and Composition, UNTEmail yang diverifikasi di unt.edu
Cormac ToherAssistant ProfessorEmail yang diverifikasi di utdallas.edu
wahyu setyawanPacific Northwest National LaboratoryEmail yang diverifikasi di pnnl.gov
Stefano SanvitoTrinity College DublinEmail yang diverifikasi di tcd.ie
Oliver BecksteinCenter for Biological Physics, Department of Physics, Arizona State UniversityEmail yang diverifikasi di asu.edu
Wiley S MorganGraduate Student Brigham Young UniversityEmail yang diverifikasi di byu.edu
Kesong YangProfessor, University of California San DiegoEmail yang diverifikasi di ucsd.edu
Alexander ShapeevSkolkovo Institute of Science and TechnologyEmail yang diverifikasi di shapeev.com
Natalio MingoCEA-GrenobleEmail yang diverifikasi di cea.fr
Brayden BekkerBYU, Brigham Young University, PhysicsEmail yang diverifikasi di byu.edu
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeEmail yang diverifikasi di cam.ac.uk
Volker BlumAssociate Professor, MEMS Department - Duke University, Durham, NCEmail yang diverifikasi di duke.edu
David HicksDuke UniversityEmail yang diverifikasi di duke.edu
Vidvuds OzolinsProfessor of Applied Physics, Yale UniversityEmail yang diverifikasi di yale.edu
Corey OsesJohns Hopkins UniversityEmail yang diverifikasi di jhu.edu

Ikuti

Gus L. W. Hart

Brigham Young University, Physics and Astronomy

Email yang diverifikasi di byu.edu - Beranda

Biophysics algorithm development applied linear algebra machine learning computational science


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
The high-throughput highway to computational materials design S Curtarolo, GLW Hart, MB Nardelli, N Mingo, S Sanvito, O Levy Nature materials 12 (3), 191-201, 2013	2134	2013
AFLOW: An automatic framework for high-throughput materials discovery S Curtarolo, W Setyawan, GLW Hart, M Jahnatek, RV Chepulskii, ... Computational Materials Science 58, 218-226, 2012	1411	2012
AFLOWLIB. ORG: A distributed materials properties repository from high-throughput ab initio calculations S Curtarolo, W Setyawan, S Wang, J Xue, K Yang, RH Taylor, LJ Nelson, ... Computational Materials Science 58, 227-235, 2012	1215	2012
First-principles elastic constants and electronic structure of and PtSi O Beckstein, JE Klepeis, GLW Hart, O Pankratov Physical Review B 63 (13), 134112, 2001	514	2001
Machine learning for alloys GLW Hart, T Mueller, C Toher, S Curtarolo Nature Reviews Materials 6 (8), 730-755, 2021	484	2021
Algorithm for generating derivative structures GLW Hart, RW Forcade Physical Review B—Condensed Matter and Materials Physics 77 (22), 224115, 2008	453	2008
Evolutionary approach for determining first-principles hamiltonians GLW Hart, V Blum, MJ Walorski, A Zunger Nature materials 4 (5), 391-394, 2005	375	2005
The AFLOW standard for high-throughput materials science calculations CE Calderon, JJ Plata, C Toher, C Oses, O Levy, M Fornari, A Natan, ... Computational Materials Science 108, 233-238, 2015	332	2015
Accelerating high-throughput searches for new alloys with active learning of interatomic potentials K Gubaev, EV Podryabinkin, GLW Hart, AV Shapeev Computational Materials Science 156, 148-156, 2019	329	2019
Compressive sensing as a paradigm for building physics models LJ Nelson, GLW Hart, F Zhou, V Ozoliņš Physical Review B—Condensed Matter and Materials Physics 87 (3), 035125, 2013	275	2013
The AFLOW library of crystallographic prototypes: part 1 MJ Mehl, D Hicks, C Toher, O Levy, RM Hanson, G Hart, S Curtarolo Computational Materials Science 136, S1-S828, 2017	259	2017
Using genetic algorithms to map first-principles results to model Hamiltonians: Application to the generalized Ising model for alloys V Blum, GLW Hart, MJ Walorski, A Zunger Physical Review B—Condensed Matter and Materials Physics 72 (16), 165113, 2005	205	2005
UNCLE: a code for constructing cluster expansions for arbitrary lattices with minimal user-input D Lerch, O Wieckhorst, GLW Hart, RW Forcade, S Müller Modelling and Simulation in Materials Science and Engineering 17 (5), 055003, 2009	194	2009
Generating derivative structures from multilattices: Algorithm and application to hcp alloys GLW Hart, RW Forcade Physical Review B—Condensed Matter and Materials Physics 80 (1), 014120, 2009	179	2009
Machine-learned multi-system surrogate models for materials prediction C Nyshadham, M Rupp, B Bekker, AV Shapeev, T Mueller, ... npj Computational Materials 5 (1), 51, 2019	142	2019
Generating derivative structures at a fixed concentration GLW Hart, LJ Nelson, RW Forcade Computational Materials Science 59, 101-107, 2012	138	2012
Electronic structure of BAs and boride III-V alloys GLW Hart, A Zunger Physical Review B 62 (20), 13522, 2000	136	2000
Comprehensive search for new phases and compounds in binary alloy systems based on platinum-group metals, using a computational first-principles approach GLW Hart, S Curtarolo, TB Massalski, O Levy Physical Review X 3 (4), 041035, 2013	130	2013
The AFLOW library of crystallographic prototypes: part 2 D Hicks, MJ Mehl, E Gossett, C Toher, O Levy, RM Hanson, G Hart, ... Computational Materials Science 161, S1-S1011, 2019	128	2019
Hafnium binary alloys from experiments and first principles O Levy, GLW Hart, S Curtarolo Acta Materialia 58 (8), 2887-2897, 2010	124	2010

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