| Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8, 13890, 2017 | 558 | 2017 |
| Machine learning of accurate energy-conserving molecular force fields S Chmiela, A Tkatchenko, HE Sauceda, I Poltavsky, KT Schütt, KR Müller Science Advances 3 (5), e1603015, 2017 | 346 | 2017 |
| SchNet: A continuous-filter convolutional neural network for modeling quantum interactions K Schütt, PJ Kindermans, HES Felix, S Chmiela, A Tkatchenko, KR Müller Advances in Neural Information Processing Systems, 992-1002, 2017 | 179 | 2017 |
| Towards exact molecular dynamics simulations with machine-learned force fields S Chmiela, HE Sauceda, KR Müller, A Tkatchenko Nature Communications 9 (1), 3887, 2018 | 135 | 2018 |
| sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning S Chmiela, HE Sauceda, I Poltavsky, KR Müller, A Tkatchenko Computer Physics Communications, 38-45, 2019 | 34 | 2019 |
| Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko The Journal of Chemical Physics, 114102, 2019 | 23 | 2019 |
| Towards exact molecular dynamics simulations with invariant machine-learned models S Chmiela | 5 | 2019 |
| Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko arXiv preprint arXiv:1909.08565, 2019 | 4 | 2019 |
| Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach J Wang, S Chmiela, KR Müller, F Noé, C Clementi The Journal of Chemical Physics 152 (19), 194106, 2020 | 3 | 2020 |
| Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches S Chmiela, HE Sauceda, A Tkatchenko, KR Müller arXiv preprint arXiv:1912.06401, 2019 | 2 | 2019 |
| Machine Learning for Molecules and Materials S Chmiela, JM Hernández-Lobato, KT Schütt, A Aspuru-Guzik, ... | | 2017 |
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Bestätigte E-Mail-Adresse bei tu-berlin.de
