Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8, 13890, 2017 | 1113 | 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 | 818 | 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 | 673 | 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 | 464 | 2018 |

Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... Chemical Reviews 121 (16), 10142-10186, 2021 | 339 | 2021 |

Combining machine learning and computational chemistry for predictive insights into chemical systems JA Keith, V Vassilev-Galindo, B Cheng, S Chmiela, M Gastegger, ... Chemical reviews 121 (16), 9816-9872, 2021 | 169 | 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 | 132 | 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 | 83 | 2019 |

SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller Nature communications 12 (1), 1-14, 2021 | 73 | 2021 |

Machine Learning Meets Quantum Physics KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ... Springer International Publishing, 2020 | 69 | 2020 |

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 | 38 | 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 | 25 | 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 | 24 | 2020 |

BIGDML—Towards accurate quantum machine learning force fields for materials HE Sauceda, LE Gálvez-González, S Chmiela, LO Paz-Borbón, KR Müller, ... Nature communications 13 (1), 1-16, 2022 | 14 | 2022 |

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 | 12 | 2020 |

Towards exact molecular dynamics simulations with invariant machine-learned models S Chmiela PQDT-Global, 2019 | 11 | 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 | 9 | 2020 |

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 | 3 | 2022 |

Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields NF Schmitz, KR Müller, S Chmiela The Journal of Physical Chemistry Letters 13, 10183-10189, 2022 | 3 | 2022 |

Detect the Interactions that Matter in Matter: Geometric Attention for Many-Body Systems T Frank, S Chmiela arXiv preprint arXiv:2106.02549, 2021 | 2 | 2021 |