James Kermode

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Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Alessandro De VitaKing's College LondonVerified email at kcl.ac.uk
Albert P. BartokUniversity of WarwickVerified email at warwick.ac.uk
Christoph OrtnerUniversity of British ColumbiaVerified email at math.ubc.ca
Dov ShermanProfessor, School of Mechanical Engineering, Tel-Aviv University, Tel-Aviv, IsraelVerified email at tauex.tau.ac.il
Zhenwei Li ([email protected])University of BaselVerified email at unibas.ch
Petr GrigorevCentre Interdisciplinaire de Nanoscience de MarseilleVerified email at cinam.univ-mrs.fr
Peter GumbschProfessor Karlsruhe Institute of Technology KITVerified email at kit.edu
Thomas D SwinburneCNRS Researcher, CINaM, MarseilleVerified email at cnrs.fr
Tristan AlbaretUniversité Lyon 1Verified email at univ-lyon1.fr
David BombacUniversity of LjubljanaVerified email at ntf.uni-lj.si
OLGA BARRERAOxford Brookes UniversityVerified email at eng.ox.ac.uk
peng gongThe University of Manchester, The University of SheffieldVerified email at manchester.ac.uk
Sandip DeBASFVerified email at basf.com
Michele CeriottiAssociate Professor at EPFL, Institute of MaterialsVerified email at epfl.ch
Carl PoelkingUniversity of CambridgeVerified email at cam.ac.uk
James P DarbyUniversity of CambridgeVerified email at cam.ac.uk
Berk OnatBIOVIA R&D Scientific Software Engineering SpecialistVerified email at 3ds.com
Eason Yi-Sheng Chen (陳翊昇)Nanyang Technological UniversityVerified email at ntu.edu.sg
Lars PastewkaUniversity of FreiburgVerified email at imtek.uni-freiburg.de

James Kermode

Professor of Materials Modelling, School of Engineering, University of Warwick

Verified email at warwick.ac.uk - Homepage

Materials Modelling Computational materials science QM/MM Fracture Predictive Modelling


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The atomic simulation environment—a Python library for working with atoms AH Larsen, JJ Mortensen, J Blomqvist, IE Castelli, R Christensen, ... Journal of Physics: Condensed Matter 29 (27), 273002, 2017	4008	2017
Machine learning unifies the modeling of materials and molecules AP Bartók, S De, C Poelking, N Bernstein, JR Kermode, G Csányi, ... Science advances 3 (12), e1701816, 2017	734	2017
Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces Z Li, JR Kermode, A De Vita Physical review letters 114 (9), 096405, 2015	721	2015
Machine learning a general-purpose interatomic potential for silicon AP Bartók, J Kermode, N Bernstein, G Csányi Physical Review X 8 (4), 041048, 2018	625	2018
Understanding and mitigating hydrogen embrittlement of steels: a review of experimental, modelling and design progress from atomistic to continuum O Barrera, D Bombac, Y Chen, TD Daff, E Galindo-Nava, P Gong, D Haley, ... Journal of materials science 53 (9), 6251-6290, 2018	424	2018
Low-speed fracture instabilities in a brittle crystal JR Kermode, T Albaret, D Sherman, N Bernstein, P Gumbsch, MC Payne, ... Nature 455 (7217), 1224-1227, 2008	247	2008
Hybrid atomistic simulation methods for materials systems N Bernstein, JR Kermode, G Csanyi Reports on Progress in Physics 72 (2), 026501, 2009	198	2009
Atomistic aspects of fracture E Bitzek, JR Kermode, P Gumbsch International Journal of Fracture 191, 13-30, 2015	185	2015
A foundation model for atomistic materials chemistry I Batatia, P Benner, Y Chiang, AM Elena, DP Kovács, J Riebesell, ... arXiv preprint arXiv:2401.00096, 2023	151	2023
A universal preconditioner for simulating condensed phase materials D Packwood, J Kermode, L Mones, N Bernstein, J Woolley, N Gould, ... The Journal of Chemical Physics 144 (16), 2016	81	2016
In situ stable crack growth at the micron scale G Sernicola, T Giovannini, P Patel, JR Kermode, DS Balint, TB Britton, ... Nature Communications 8 (1), 108, 2017	74	2017
Efficient and transferable machine learning potentials for the simulation of crystal defects in bcc Fe and W AM Goryaeva, J Dérès, C Lapointe, P Grigorev, TD Swinburne, ... Physical Review Materials 5 (10), 103803, 2021	60	2021
Macroscopic scattering of cracks initiated at single impurity atoms JR Kermode, L Ben-Bashat, F Atrash, JJ Cilliers, D Sherman, A De Vita Nature communications 4 (1), 2441, 2013	60	2013
Expressive programming for computational physics in Fortran 950+ G Csányi, S Winfield, J Kermode, MC Payne, A Comisso, A De Vita, ... Newsletter of the Computational Physics Group, 1-24, 2007	52	2007
f90wrap: an automated tool for constructing deep Python interfaces to modern Fortran codes JR Kermode Journal of Physics: Condensed Matter 32 (30), 305901, 2020	51	2020
A framework for machine‐learning‐augmented multiscale atomistic simulations on parallel supercomputers M Caccin, Z Li, JR Kermode, A De Vita International Journal of Quantum Chemistry 115 (16), 1129-1139, 2015	47	2015
Dissociative Chemisorption of Inducing Stress Corrosion Cracking in Silicon Crystals A Gleizer, G Peralta, JR Kermode, A De Vita, D Sherman Physical Review Letters 112 (11), 115501, 2014	43	2014
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models L Zhang, B Onat, G Dusson, A McSloy, G Anand, RJ Maurer, C Ortner, ... Npj Computational Materials 8 (1), 158, 2022	42	2022
Sensitivity and dimensionality of atomic environment representations used for machine learning interatomic potentials B Onat, C Ortner, JR Kermode The Journal of Chemical Physics 153 (14), 2020	42	2020
Compressing local atomic neighbourhood descriptors JP Darby, JR Kermode, G Csányi npj Computational Materials 8 (1), 166, 2022	40	2022

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