| Machine learning for electronically excited states of molecules J Westermayr, P Marquetand Chemical Reviews 121 (16), 9873-9926, 2020 | 268 | 2020 |
| 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 | 179 | 2019 |
| 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 | 146 | 2020 |
| 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 | 141 | 2021 |
| 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 | 102 | 2022 |
| Neural networks and kernel ridge regression for excited states dynamics of CH2NH: From single-state to multi-state representations and multi-property machine learning models J Westermayr, FA Faber, AS Christensen, OA von Lilienfeld, ... Machine Learning: Science and Technology 1 (2), 025009, 2020 | 68 | 2020 |
| Machine learning and excited-state molecular dynamics J Westermayr, P Marquetand Machine Learning: Science and Technology 1 (4), 043001, 2020 | 63 | 2020 |
| Deep learning for UV absorption spectra with SchNarc: First steps toward transferability in chemical compound space J Westermayr, P Marquetand The Journal of Chemical Physics 153 (15), 2020 | 59 | 2020 |
| Physically inspired deep learning of molecular excitations and photoemission spectra J Westermayr, RJ Maurer Chemical Science 12 (32), 10755-10764, 2021 | 43 | 2021 |
| High-throughput property-driven generative design of functional organic molecules J Westermayr, J Gilkes, R Barrett, RJ Maurer Nature Computational Science 3 (2), 139-148, 2023 | 30 | 2023 |
| Deep learning study of tyrosine reveals that roaming can lead to photodamage J Westermayr, M Gastegger, D Vörös, L Panzenboeck, F Joerg, ... Nature Chemistry 14 (8), 914-919, 2022 | 29 | 2022 |
| BuRNN: Buffer region neural network approach for polarizable-embedding neural network/molecular mechanics simulations B Lier, P Poliak, P Marquetand, J Westermayr, C Oostenbrink The Journal of Physical Chemistry Letters 13 (17), 3812-3818, 2022 | 24 | 2022 |
| Long-range dispersion-inclusive machine learning potentials for structure search and optimization of hybrid organic–inorganic interfaces J Westermayr, S Chaudhuri, A Jeindl, OT Hofmann, RJ Maurer Digital Discovery 1 (4), 463-475, 2022 | 20 | 2022 |
| Tridentate 3-substituted naphthoquinone ruthenium arene complexes: Synthesis, characterization, aqueous behavior, and theoretical and biological studies H Geisler, J Westermayr, K Cseh, D Wenisch, V Fuchs, S Harringer, ... Inorganic Chemistry 60 (13), 9805-9819, 2021 | 10 | 2021 |
| NQCDynamics. jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase J Gardner, OA Douglas-Gallardo, WG Stark, J Westermayr, SM Janke, ... The Journal of Chemical Physics 156 (17), 2022 | 9 | 2022 |
| Arene variation of highly cytotoxic tridentate naphthoquinone-based ruthenium (II) complexes and in-depth in vitro studies K Cseh, H Geisler, K Stanojkovska, J Westermayr, P Brunmayr, ... Pharmaceutics 14 (11), 2466, 2022 | 8 | 2022 |
| Machine learning for nonadiabatic molecular dynamics J Westermayr, P Marquetand | 3 | 2020 |
| Machine Learning Interatomic Potentials for Reactive Hydrogen Dynamics at Metal Surfaces Based on Iterative Refinement of Reaction Probabilities WG Stark, J Westermayr, OA Douglas-Gallardo, J Gardner, S Habershon, ... The Journal of Physical Chemistry C 127 (50), 24168-24182, 2023 | 2* | 2023 |
| Machine learning for accelerated bandgap prediction in strain-engineered quaternary III–V semiconductors B Mondal, J Westermayr, R Tonner-Zech The Journal of Chemical Physics 159 (10), 2023 | 2 | 2023 |
| Decoding energy decomposition analysis: Machine‐learned Insights on the impact of the density functional on the bonding analysis T Oestereich, R Tonner‐Zech, J Westermayr Journal of Computational Chemistry 45 (7), 368-376, 2024 | 1 | 2024 |
Philipp MarquetandVienna, AustriaBestätigte E-Mail-Adresse bei marquetand.net
