| Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schutt, ... Chemical Reviews 121 (16), 10142-10186, 2021 | 1172 | 2021 |
| Combining machine learning and computational chemistry for predictive insights into chemical systems JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ... Chemical reviews 121 (16), 9816-9872, 2021 | 662 | 2021 |
| Equivariant message passing for the prediction of tensorial properties and molecular spectra K Schütt, O Unke, M Gastegger International Conference on Machine Learning, 9377-9388, 2021 | 631 | 2021 |
| Machine learning molecular dynamics for the simulation of infrared spectra M Gastegger, J Behler, P Marquetand Chemical science 8 (10), 6924-6935, 2017 | 539 | 2017 |
| Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions KT Schütt, M Gastegger, A Tkatchenko, KR Müller, RJ Maurer Nature communications 10 (1), 5024, 2019 | 487 | 2019 |
| SchNetPack: A deep learning toolbox for atomistic systems KT Schütt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller Journal of chemical theory and computation 15 (1), 448-455, 2018 | 450 | 2018 |
| wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials M Gastegger, L Schwiedrzik, M Bittermann, F Berzsenyi, P Marquetand The Journal of chemical physics 148 (24), 2018 | 323 | 2018 |
| SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller Nature communications 12 (1), 7273, 2021 | 284 | 2021 |
| Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules N Gebauer, M Gastegger, K Schütt Advances in neural information processing systems 32, 2019 | 258 | 2019 |
| Machine learning enables long time scale molecular photodynamics simulations J Westermayr, M Gastegger, MFSJ Menger, S Mai, L González, ... Chemical science 10 (35), 8100-8107, 2019 | 198 | 2019 |
| Inverse design of 3d molecular structures with conditional generative neural networks NWA Gebauer, M Gastegger, SSP Hessmann, KR Müller, KT Schütt Nature communications 13 (1), 973, 2022 | 194 | 2022 |
| Perspective on integrating machine learning into computational chemistry and materials science J Westermayr, M Gastegger, KT Schütt, RJ Maurer The Journal of Chemical Physics 154 (23), 2021 | 193 | 2021 |
| Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics J Westermayr, M Gastegger, P Marquetand The journal of physical chemistry letters 11 (10), 3828-3834, 2020 | 174 | 2020 |
| Roadmap on machine learning in electronic structure HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ... Electronic Structure 4 (2), 023004, 2022 | 152 | 2022 |
| High-dimensional neural network potentials for organic reactions and an improved training algorithm M Gastegger, P Marquetand Journal of chemical theory and computation 11 (5), 2187-2198, 2015 | 149 | 2015 |
| Machine learning of solvent effects on molecular spectra and reactions M Gastegger, KT Schütt, KR Müller Chemical science 12 (34), 11473-11483, 2021 | 123 | 2021 |
| SE (3)-equivariant prediction of molecular wavefunctions and electronic densities O Unke, M Bogojeski, M Gastegger, M Geiger, T Smidt, KR Müller Advances in Neural Information Processing Systems 34, 14434-14447, 2021 | 104 | 2021 |
| Fast protein backbone generation with se (3) flow matching J Yim, A Campbell, AYK Foong, M Gastegger, J Jiménez-Luna, S Lewis, ... arXiv preprint arXiv:2310.05297, 2023 | 65 | 2023 |
| Comparing the accuracy of high-dimensional neural network potentials and the systematic molecular fragmentation method: A benchmark study for all-trans alkanes M Gastegger, C Kauffmann, J Behler, P Marquetand The Journal of chemical physics 144 (19), 2016 | 65 | 2016 |
| A deep neural network for molecular wave functions in quasi-atomic minimal basis representation M Gastegger, A McSloy, M Luya, KT Schütt, RJ Maurer The Journal of Chemical Physics 153 (4), 2020 | 60 | 2020 |
Kristof T. SchüttPfizer, Machine Learning ResearchEmail yang diverifikasi di pfizer.com