| 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 | 1066 | 2010 |
| On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B 87 (18), 184115, 2013 | 819 | 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 | 652 | 2004 |
| 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 | 544 | 2008 |
| 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 | 479 | 2005 |
| 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 | 317 | 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 | 304 | 2017 |
| 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 | 301 | 2005 |
| “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 (17), 175503, 2004 | 289 | 2004 |
| G aussian approximation potentials: A brief tutorial introduction AP Bartók, G Csányi International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015 | 285 | 2015 |
| Chemically active substitutional nitrogen impurity in carbon nanotubes AH Nevidomskyy, G Csányi, MC Payne Physical review letters 91 (10), 105502, 2003 | 282 | 2003 |
| Machine learning based interatomic potential for amorphous carbon VL Deringer, G Csányi Physical Review B 95 (9), 094203, 2017 | 263 | 2017 |
| 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 | 215 | 2016 |
| 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 | 191 | 2008 |
| Gaussian processes: a method for automatic QSAR modeling of ADME properties O Obrezanova, G Csányi, JMR Gola, MD Segall Journal of chemical information and modeling 47 (5), 1847-1857, 2007 | 191 | 2007 |
| Accuracy and transferability of Gaussian approximation potential models for tungsten WJ Szlachta, AP Bartók, G Csányi Physical Review B 90 (10), 104108, 2014 | 169 | 2014 |
| Structure of a large social network G Csányi, B Szendrői Physical Review E 69 (3), 036131, 2004 | 167 | 2004 |
| Hybrid atomistic simulation methods for materials systems N Bernstein, JR Kermode, G Csanyi Reports on Progress in Physics 72 (2), 026501, 2009 | 164 | 2009 |
| Machine-learning approach for one-and two-body corrections to density functional theory: Applications to molecular and condensed water AP Bartók, MJ Gillan, FR Manby, G Csányi Physical Review B 88 (5), 054104, 2013 | 160 | 2013 |
| Machine learning a general-purpose interatomic potential for silicon AP Bartók, J Kermode, N Bernstein, G Csányi Physical Review X 8 (4), 041048, 2018 | 159 | 2018 |
