| 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 | 2464 | 2010 |
| On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B 87 (18), 184115, 2013 | 2178 | 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 | 746 | 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 | 675 | 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 | 644 | 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 | 618 | 2008 |
| 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 | 584 | 2020 |
| G aussian approximation potentials: A brief tutorial introduction AP Bartók, G Csányi International Journal of Quantum Chemistry 115 (16), 1051-1057, 2015 | 584 | 2015 |
| Machine learning based interatomic potential for amorphous carbon VL Deringer, G Csányi Physical Review B 95 (9), 094203, 2017 | 578 | 2017 |
| 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 | 555 | 2021 |
| Machine learning interatomic potentials as emerging tools for materials science VL Deringer, MA Caro, G Csányi Advanced Materials 31 (46), 1902765, 2019 | 549 | 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 | 548 | 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 | 529 | 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 | 400 | 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 | 354 | 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 (17), 175503, 2004 | 354 | 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 | 342 | 2005 |
| Accuracy and transferability of Gaussian approximation potential models for tungsten WJ Szlachta, AP Bartók, G Csányi Physical Review B 90 (10), 104108, 2014 | 298 | 2014 |
| Chemically active substitutional nitrogen impurity in carbon nanotubes AH Nevidomskyy, G Csányi, MC Payne Physical review letters 91 (10), 105502, 2003 | 296 | 2003 |
| Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron D Dragoni, TD Daff, G Csányi, N Marzari Physical Review Materials 2 (1), 013808, 2018 | 263 | 2018 |
