Gabor Csanyi
Gabor Csanyi
Professor of Molecular Modelling, Engineering Laboratory, University of Cambridge
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Cited by
Cited by
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
AP Bartók, MC Payne, R Kondor, G Csányi
Physical review letters 104 (13), 136403, 2010
On representing chemical environments
AP Bartók, R Kondor, G Csányi
Physical Review B 87 (18), 184115, 2013
Reinforcement of single-walled carbon nanotube bundles by intertube bridging
A Kis, G Csanyi, JP Salvetat, TN Lee, E Couteau, AJ Kulik, W Benoit, ...
Nature materials 3 (3), 153-157, 2004
Comparing molecules and solids across structural and alchemical space
S De, AP Bartók, G Csányi, M Ceriotti
Physical Chemistry Chemical Physics 18 (20), 13754-13769, 2016
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
Edge-functionalized and substitutionally doped graphene nanoribbons: Electronic and spin properties
F Cervantes-Sodi, G Csányi, S Piscanec, AC Ferrari
Physical Review B 77 (16), 165427, 2008
Gaussian process regression for materials and molecules
VL Deringer, AP Bartók, N Bernstein, DM Wilkins, M Ceriotti, G Csányi
Chemical Reviews 121 (16), 10073-10141, 2021
Machine learning based interatomic potential for amorphous carbon
VL Deringer, G Csányi
Physical Review B 95 (9), 094203, 2017
Performance and cost assessment of machine learning interatomic potentials
Y Zuo, C Chen, X Li, Z Deng, Y Chen, J Behler, G Csányi, AV Shapeev, ...
The Journal of Physical Chemistry A 124 (4), 731-745, 2020
G aussian approximation potentials: A brief tutorial introduction
AP Bartók, G Csányi
International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015
Machine learning interatomic potentials as emerging tools for materials science
VL Deringer, MA Caro, G Csányi
Advanced Materials 31 (46), 1902765, 2019
Surface diffusion: the low activation energy path for nanotube growth
S Hofmann, G Csanyi, AC Ferrari, MC Payne, J Robertson
Physical review letters 95 (3), 036101, 2005
Machine learning a general-purpose interatomic potential for silicon
AP Bartók, J Kermode, N Bernstein, G Csányi
Physical Review X 8 (4), 041048, 2018
Modeling molecular interactions in water: From pairwise to many-body potential energy functions
GA Cisneros, KT Wikfeldt, L Ojamäe, J Lu, Y Xu, H Torabifard, AP Bartók, ...
Chemical reviews 116 (13), 7501-7528, 2016
Physics-inspired structural representations for molecules and materials
F Musil, A Grisafi, AP Bartók, C Ortner, G Csányi, M Ceriotti
Chemical Reviews 121 (16), 9759-9815, 2021
" Learn on the fly": A hybrid classical and quantum-mechanical molecular dynamics simulation
G Csányi, T Albaret, MC Payne, A De Vita
Physical review letters 93, 175503-175503, 2004
The role of the interlayer state in the electronic structure of superconducting graphite intercalated compounds
G Csányi, PB Littlewood, AH Nevidomskyy, CJ Pickard, BD Simons
Nature Physics 1 (1), 42-45, 2005
Accuracy and transferability of Gaussian approximation potential models for tungsten
WJ Szlachta, AP Bartók, G Csányi
Physical Review B 90 (10), 104108, 2014
Chemically active substitutional nitrogen impurity in carbon nanotubes
AH Nevidomskyy, G Csányi, MC Payne
Physical review letters 91 (10), 105502, 2003
MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
I Batatia, DP Kovács, GNC Simm, C Ortner, G Csányi
Advances in Neural Information Processing Systems (NeurIPS) 2022, 2022
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