| Machine learning in QM/MM molecular dynamics simulations of condensed-phase systems L Böselt, M Thürlemann, S Riniker Journal of Chemical Theory and Computation 17 (5), 2641-2658, 2021 | 122 | 2021 |
| Regularized by physics: Graph neural network parametrized potentials for the description of intermolecular interactions M Thurlemann, L Boselt, S Riniker Journal of Chemical Theory and Computation 19 (2), 562-579, 2023 | 18 | 2023 |
| Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems M Thürlemann, S Riniker Chemical Science 14 (44), 12661-12675, 2023 | 10 | 2023 |
| Energy-based clustering: Fast and robust clustering of data with known likelihood functions M Thürlemann, S Riniker The Journal of Chemical Physics 159 (2), 2023 | 6 | 2023 |
| Anisotropic message passing: Graph neural networks with directional and long-range interactions M Thürlemann, S Riniker The Eleventh International Conference on Learning Representations, 2023 | 6 | 2023 |
| Neural Network Potential with Multi-Resolution Approach Enables Accurate Prediction of Reaction Free Energies in Solution F Pultar, M Thuerlemann, I Gordiy, E Doloszeski, S Riniker arXiv preprint arXiv:2411.19728, 2024 | 1 | 2024 |
| Learning Atomic Multipoles: Prediction of the Electrostatic Potential with Equivariant Graph Neural Networks M Thürlemann, L Böselt, S Riniker J. Chem. Theory Comput. 18 (3), 1701–1710, 2022 | | 2022 |
