| 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 | 1754 | 2010 |
| On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B 87 (18), 184115, 2013 | 1517 | 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 | 709 | 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 | 599 | 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 | 524 | 2005 |
| 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 | 519 | 2017 |
| 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 | 518 | 2016 |
| G aussian approximation potentials: A brief tutorial introduction AP Bartók, G Csányi International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015 | 468 | 2015 |
| Machine learning based interatomic potential for amorphous carbon VL Deringer, G Csányi Physical Review B 95 (9), 094203, 2017 | 436 | 2017 |
| Machine learning a general-purpose interatomic potential for silicon AP Bartók, J Kermode, N Bernstein, G Csányi Physical Review X 8 (4), 041048, 2018 | 353 | 2018 |
| “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 | 333 | 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 | 327 | 2005 |
| 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 | 325 | 2016 |
| 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 | 320 | 2020 |
| Chemically active substitutional nitrogen impurity in carbon nanotubes AH Nevidomskyy, G Csányi, MC Payne Physical review letters 91 (10), 105502, 2003 | 293 | 2003 |
| Machine learning interatomic potentials as emerging tools for materials science VL Deringer, MA Caro, G Csányi Advanced Materials 31 (46), 1902765, 2019 | 286 | 2019 |
| Accuracy and transferability of Gaussian approximation potential models for tungsten WJ Szlachta, AP Bartók, G Csányi Physical Review B 90 (10), 104108, 2014 | 244 | 2014 |
| 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 | 227 | 2007 |
| 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 | 226 | 2008 |
| 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 | 206 | 2013 |
