| Schnet–a deep learning architecture for molecules and materials KT Schütt, HE Sauceda, PJ Kindermans, A Tkatchenko, KR Müller The Journal of Chemical Physics 148 (24), 2018 | 2044 | 2018 |
| Schnet: A continuous-filter convolutional neural network for modeling quantum interactions K Schütt, PJ Kindermans, HE Sauceda Felix, S Chmiela, A Tkatchenko, ... Advances in neural information processing systems 30, 2017 | 1443 | 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 | 1342 | 2017 |
| Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... Chemical Reviews 121 (16), 10142-10186, 2021 | 1143 | 2021 |
| Towards exact molecular dynamics simulations with machine-learned force fields S Chmiela, HE Sauceda, KR Müller, A Tkatchenko Nature communications 9 (1), 3887, 2018 | 801 | 2018 |
| 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), 7273, 2021 | 275 | 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 240, 38-45, 2019 | 247 | 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 150 (11), 2019 | 131 | 2019 |
| Accurate global machine learning force fields for molecules with hundreds of atoms S Chmiela, V Vassilev-Galindo, OT Unke, A Kabylda, HE Sauceda, ... Science Advances 9 (2), eadf0873, 2023 | 123 | 2023 |
| Vibrational properties of metal nanoparticles: Atomistic simulation and comparison with time-resolved investigation HE Sauceda, D Mongin, P Maioli, A Crut, M Pellarin, N Del Fatti, F Vallée, ... The Journal of Physical Chemistry C 116 (47), 25147-25156, 2012 | 93 | 2012 |
| Mechanical vibrations of atomically defined metal clusters: from nano-to molecular-size oscillators P Maioli, T Stoll, HE Sauceda, I Valencia, A Demessence, F Bertorelle, ... Nano letters 18 (11), 6842-6849, 2018 | 74 | 2018 |
| 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), 3733, 2022 | 65 | 2022 |
| Size and shape dependence of the vibrational spectrum and low-temperature specific heat of Au nanoparticles HE Sauceda, F Salazar, LA Pérez, IL Garzón The Journal of Physical Chemistry C 117 (47), 25160-25168, 2013 | 53 | 2013 |
| 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), 2020 | 51 | 2020 |
| Structural determination of metal nanoparticles from their vibrational (phonon) density of states HE Sauceda, IL Garzón The Journal of Physical Chemistry C 119 (20), 10876-10880, 2015 | 48 | 2015 |
| 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), 442, 2021 | 47 | 2021 |
| Vibrational Spectrum, Caloric Curve, Low-Temperature Heat Capacity, and Debye Temperature of Sodium Clusters: The Na139+ Case HE Sauceda, JJ Pelayo, F Salazar, LA Pérez, IL Garzón The Journal of Physical Chemistry C 117 (21), 11393-11398, 2013 | 23 | 2013 |
| 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 | 18 | 2020 |
| 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 | 14 | 2020 |
| High-fidelity molecular dynamics trajectory reconstruction with bi-directional neural networks L Winkler, KR Müller, HE Sauceda Machine Learning: Science and Technology 3 (2), 025011, 2022 | 11 | 2022 |
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
