| High-dimensional potential energy surfaces for molecular simulations: from empiricism to machine learning OT Unke, D Koner, S Patra, S Käser, M Meuwly Machine Learning: Science and Technology 1 (1), 013001, 2020 | 74 | 2020 |
| Neural network potentials for chemistry: concepts, applications and prospects S Käser, LI Vazquez-Salazar, M Meuwly, K Töpfer Digital Discovery 2 (1), 28-58, 2023 | 72 | 2023 |
| Reactive Dynamics and Spectroscopy of Hydrogen Transfer from Neural Network-Based Reactive Potential Energy Surfaces S Käser, O Unke, M Meuwly New Journal of Physics 22 (5), 055002, 2020 | 57 | 2020 |
| Transfer learning to CCSD (T): Accurate anharmonic frequencies from machine learning models S Käser, ED Boittier, M Upadhyay, M Meuwly Journal of Chemical Theory and Computation 17 (6), 3687-3699, 2021 | 39 | 2021 |
| Optimized graphene electrodes for contacting graphene nanoribbons O Braun, J Overbeck, M El Abbassi, S Käser, R Furrer, A Olziersky, ... Carbon 184, 331-339, 2021 | 38 | 2021 |
| Machine Learning Models of Vibrating H2CO: Comparing Reproducing Kernels, FCHL, and PhysNet S Käser, D Koner, AS Christensen, OA von Lilienfeld, M Meuwly The Journal of Physical Chemistry A 124 (42), 8853-8865, 2020 | 31 | 2020 |
| Isomerization and decomposition reactions of acetaldehyde relevant to atmospheric processes from dynamics simulations on neural network-based potential energy surfaces S Käser, OT Unke, M Meuwly The Journal of chemical physics 152 (21), 2020 | 31 | 2020 |
| Transfer learned potential energy surfaces: accurate anharmonic vibrational dynamics and dissociation energies for the formic acid monomer and dimer S Käser, M Meuwly Physical Chemistry Chemical Physics 24 (9), 5269-5281, 2022 | 30 | 2022 |
| Transfer learning for affordable and high-quality tunneling splittings from instanton calculations S Käser, JO Richardson, M Meuwly Journal of Chemical Theory and Computation 18 (11), 6840-6850, 2022 | 22 | 2022 |
| Double proton transfer in hydrated formic acid dimer: Interplay of spatial symmetry and solvent-generated force on reactivity K Töpfer, S Käser, M Meuwly Physical Chemistry Chemical Physics 24 (22), 13869-13882, 2022 | 19 | 2022 |
| The first HyDRA challenge for computational vibrational spectroscopy TL Fischer, M Bödecker, SM Schweer, J Dupont, V Lepère, ... Physical Chemistry Chemical Physics 25 (33), 22089-22102, 2023 | 18 | 2023 |
| Machine learning for observables: Reactant to product state distributions for atom–diatom collisions J Arnold, D Koner, S Käser, N Singh, RJ Bemish, M Meuwly The Journal of Physical Chemistry A 124 (35), 7177-7190, 2020 | 17 | 2020 |
| CHARMM at 45: Enhancements in accessibility, functionality, and speed W Hwang, SL Austin, A Blondel, ED Boittier, S Boresch, M Buck, ... The Journal of Physical Chemistry B 128 (41), 9976-10042, 2024 | 14 | 2024 |
| PhysNet meets CHARMM: A framework for routine machine learning/molecular mechanics simulations K Song, S Käser, K Töpfer, LI Vazquez-Salazar, M Meuwly The Journal of Chemical Physics 159 (2), 2023 | 12 | 2023 |
| Transfer-learned potential energy surfaces: Toward microsecond-scale molecular dynamics simulations in the gas phase at CCSD (T) quality S Käser, M Meuwly The Journal of Chemical Physics 158 (21), 2023 | 12 | 2023 |
| Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023 I Poltavsky, M Puleva, A Charkin-Gorbulin, G Fonseca, I Batatia, ... Chemical Science, 2025 | 6 | 2025 |
| Conformational and state-specific effects in reactions of 2, 3-dibromobutadiene with Coulomb-crystallized calcium ions A Kilaj, S Käser, J Wang, P Straňák, M Schwilk, L Xu, OA von Lilienfeld, ... Physical Chemistry Chemical Physics 25 (20), 13933-13945, 2023 | 6 | 2023 |
| Hydration dynamics and IR spectroscopy of 4-fluorophenol SM Salehi, S Käser, K Töpfer, P Diamantis, R Pfister, P Hamm, ... Physical Chemistry Chemical Physics 24 (42), 26046-26060, 2022 | 4 | 2022 |
| Effects of aleatoric and epistemic errors in reference data on the learnability and quality of NN-based potential energy surfaces S Goswami, S Käser, RJ Bemish, M Meuwly Artificial Intelligence Chemistry 2 (1), 100033, 2024 | 2 | 2024 |
| Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces S Käser, M Meuwly The Journal of Physical Chemistry Letters 15 (12), 3419-3424, 2024 | 2 | 2024 |
M MeuwlyUniversity of BaselVerified email at unibas.ch