| Machine learning molecular dynamics for the simulation of infrared spectra M Gastegger, J Behler, P Marquetand Chemical science 8 (10), 6924-6935, 2017 | 199 | 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), 1-10, 2019 | 101 | 2019 |
| 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), 241709, 2018 | 101 | 2018 |
| SchNetPack: A deep learning toolbox for atomistic systems KT Schutt, P Kessel, M Gastegger, KA Nicoli, A Tkatchenko, KR Müller Journal of chemical theory and computation 15 (1), 448-455, 2018 | 95 | 2018 |
| 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 | 84 | 2015 |
| 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 | 48 | 2019 |
| 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), 194110, 2016 | 40 | 2016 |
| Loganin and secologanin derived tryptamine–iridoid alkaloids from Palicourea crocea and Palicourea padifolia (Rubiaceae) A Berger, MK Kostyan, SI Klose, M Gastegger, E Lorbeer, L Brecker, ... Phytochemistry 116, 162-169, 2015 | 24 | 2015 |
| 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 | 20 | 2020 |
| Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules NWA Gebauer, M Gastegger, KT Schütt arXiv preprint arXiv:1906.00957, 2019 | 18 | 2019 |
| Quantum-chemical insights from interpretable atomistic neural networks KT Schütt, M Gastegger, A Tkatchenko, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 311-330, 2019 | 15 | 2019 |
| Generating equilibrium molecules with deep neural networks NWA Gebauer, M Gastegger, KT Schütt arXiv preprint arXiv:1810.11347, 2018 | 15 | 2018 |
| Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... arXiv preprint arXiv:2010.07067, 2020 | 5 | 2020 |
| 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), 044123, 2020 | 4 | 2020 |
| Molecular dynamics with neural network potentials M Gastegger, P Marquetand Machine Learning Meets Quantum Physics, 233-252, 2020 | 4 | 2020 |
| Exploring density functional subspaces with genetic algorithms M Gastegger, L González, P Marquetand Monatshefte für Chemie-Chemical Monthly 150 (2), 173-182, 2019 | 4 | 2019 |
| Molecular dynamics with neural-network potentials M Gastegger, P Marquetand arXiv preprint arXiv:1812.07676, 2018 | 4 | 2018 |
| Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields HE Sauceda, M Gastegger, S Chmiela, KR Müller, A Tkatchenko The Journal of Chemical Physics 153 (12), 124109, 2020 | 2 | 2020 |
| Modeling molecular spectra with interpretable atomistic neural networks M Gastegger, K Schütt, H Sauceda, KR Müller, A Tkatchenko APS March Meeting Abstracts 2019, E32. 007, 2019 | 2 | 2019 |
| Artificial Intelligence in theoretical chemistry M Gastegger Ph. D. thesis, University of Vienna, 2017 | 2 | 2017 |
Philipp MarquetandUniversity of Vienna, Institute of Theoretical Chemistry, Vienna, AustriaVerified email at univie.ac.at
