| 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 | 4047 | 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 | 739 | 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 | 723 | 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 | 630 | 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 | 426 | 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 | 186 | 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 | 165 | 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 |
| 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 | 43 | 2022 |
| 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 |
| 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 | 41 | 2022 |
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of Cambridgecam.ac.uk의 이메일 확인됨
