| Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8, 13890, 2017 | 1021 | 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 | 762 | 2017 |
| Schnet: A continuous-filter convolutional neural network for modeling quantum interactions KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ... arXiv preprint arXiv:1706.08566, 2017 | 571 | 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 | 412 | 2018 |
| Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... arXiv preprint arXiv:2010.07067, 2020 | 230 | 2020 |
| Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ... arXiv preprint arXiv:2102.06321, 2021 | 115 | 2021 |
| 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 | 115 | 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 | 75 | 2019 |
| Machine Learning Meets Quantum Physics KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ... Springer International Publishing, 2020 | 67 | 2020 |
| SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller arXiv preprint arXiv:2105.00304, 2021 | 50 | 2021 |
| 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 | 35 | 2020 |
| Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature HE Sauceda, V Vassilev-Galindo, S Chmiela, KR Müller, A Tkatchenko Nature Communications 12 (1), 1-10, 2021 | 24 | 2021 |
| 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 | 19 | 2020 |
| Construction of machine learned force fields with quantum chemical accuracy: Applications and chemical insights HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko Machine Learning Meets Quantum Physics, 277-307, 2020 | 13 | 2020 |
| Towards exact molecular dynamics simulations with invariant machine-learned models S Chmiela PQDT-Global, 2019 | 9 | 2019 |
| Accurate molecular dynamics enabled by efficient physically constrained machine learning approaches S Chmiela, HE Sauceda, A Tkatchenko, KR Müller Machine Learning Meets Quantum Physics, 129-154, 2020 | 7 | 2020 |
| BIGDML: Towards Exact Machine Learning Force Fields for Materials HE Sauceda, LE Gálvez-González, S Chmiela, LO Paz-Borbón, KR Müller, ... arXiv preprint arXiv:2106.04229, 2021 | 3 | 2021 |
| Towards Linearly Scaling and Chemically Accurate Global Machine Learning Force Fields for Large Molecules A Kabylda, V Vassilev-Galindo, S Chmiela, I Poltavsky, A Tkatchenko arXiv preprint arXiv:2209.03985, 2022 | | 2022 |
| Algorithmic Differentiation for Automatized Modelling of Machine Learned Force Fields NF Schmitz, KR Müller, S Chmiela arXiv preprint arXiv:2208.12104, 2022 | | 2022 |
| Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems T Frank, S Chmiela arXiv preprint arXiv:2106.02549, 2021 | | 2021 |
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIBestätigte E-Mail-Adresse bei tu-berlin.de
