Gus L. W. Hart

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Curtarolo, StefanoDuke UniversityVerified email at nietzsche.mems.duke.edu
Lance J. NelsonBrigham Young University - IdahoVerified email at byui.edu
Alex ZungerUniversity of Colorado ,Boulder,Colorado.Renewable and Sustainable Energy InstituteVerified email at colorado.edu
Michael MehlDuke UniversityVerified email at duke.edu
Conrad RosenbrockBrigham Young UniversityVerified email at byu.edu
Marco Buongiorno NardelliRegents Professor of Physics and Composition, UNTVerified email at unt.edu
Chandramouli NyshadhamKebotix Inc.Verified email at kebotix.com
Cormac ToherAssistant ProfessorVerified email at utdallas.edu
wahyu setyawanPacific Northwest National Laboratory, Duke Univ, Florida State Univ, Institut Teknologi BandungVerified email at pnnl.gov
Stefano SanvitoTrinity College DublinVerified email at tcd.ie
Oliver BecksteinCenter for Biological Physics, Department of Physics, Arizona State UniversityVerified email at asu.edu
Wiley S MorganGraduate Student Brigham Young UniversityVerified email at byu.edu
Kesong YangProfessor, University of California San DiegoVerified email at ucsd.edu
Alexander ShapeevSkolkovo Institute of Science and TechnologyVerified email at shapeev.com
Natalio MingoCEA-GrenobleVerified email at cea.fr
Brayden BekkerBYU, Brigham Young University, PhysicsVerified email at byu.edu
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Volker BlumAssociate Professor, MEMS Department - Duke University, Durham, NCVerified email at duke.edu
David HicksDuke UniversityVerified email at duke.edu
Vidvuds OzolinsProfessor of Applied Physics, Yale UniversityVerified email at yale.edu

Gus L. W. Hart

Brigham Young University, Physics and Astronomy

Verified email at byu.edu - Homepage

Biophysics algorithm development applied linear algebra machine learning computational science


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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	2074	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	1333	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	1171	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	502	2001
Algorithm for generating derivative structures GLW Hart, RW Forcade Physical Review B—Condensed Matter and Materials Physics 77 (22), 224115, 2008	444	2008
Machine learning for alloys GLW Hart, T Mueller, C Toher, S Curtarolo Nature Reviews Materials 6 (8), 730-755, 2021	415	2021
Evolutionary approach for determining first-principles hamiltonians GLW Hart, V Blum, MJ Walorski, A Zunger Nature materials 4 (5), 391-394, 2005	377	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	319	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	317	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	267	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	247	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	201	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	188	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	175	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	136	2019
Generating derivative structures at a fixed concentration GLW Hart, LJ Nelson, RW Forcade Computational Materials Science 59, 101-107, 2012	135	2012
Electronic structure of BAs and boride III-V alloys GLW Hart, A Zunger Physical Review B 62 (20), 13522, 2000	133	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	128	2013
Hafnium binary alloys from experiments and first principles O Levy, GLW Hart, S Curtarolo Acta Materialia 58 (8), 2887-2897, 2010	125	2010
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	124	2019

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