| Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8 (13890), 2017 | 890 | 2017 |
| 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), 241722, 2018 | 695 | 2018 |
| 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 | 609 | 2017 |
| SchNet: A continuous-filter convolutional neural network for modeling quantum interactions KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ... Advances in Neural Information Processing System 30, 992--1002, 2017 | 406 | 2017 |
| How to represent crystal structures for machine learning: Towards fast prediction of electronic properties KT Schütt, H Glawe, F Brockherde, A Sanna, KR Müller, EKU Gross Phys. Rev. B 89 (20), 205118, 2014 | 366 | 2014 |
| The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267-280, 2019 | 342 | 2019 |
| Learning how to explain neural networks: PatternNet and PatternAttribution PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne 6th International Conference on Learning Representations, 2018 | 318* | 2018 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... J. Mach. Learn. Res. 20 (93), 1-8, 2019 | 205 | 2019 |
| Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions KT Schütt, M Gastegger, A Tkatchenko, KR Müller, RJ Maurer Nature Communications 10 (1), 1-10, 2019 | 190 | 2019 |
| SchNetPack: A Deep Learning Toolbox For Atomistic Systems KT Schütt, P Kessel, M Gastegger, K Nicoli, A Tkatchenko, KR Müller Journal of chemical theory and computation 15 (1), 448-455, 2019 | 169 | 2019 |
| Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... Chemical Reviews, 2021 | 96 | 2021 |
| Investigating the influence of noise and distractors on the interpretation of neural networks PJ Kindermans, K Schütt, KR Müller, S Dähne arXiv preprint arXiv:1611.07270, 2016 | 81 | 2016 |
| Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules N Gebauer, M Gastegger, KT Schütt Advances in Neural Information Processing Systems, 7566-7578, 2019 | 48 | 2019 |
| Machine Learning Meets Quantum Physics KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ... Lecture Notes in Physics, 2020 | 44 | 2020 |
| Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning W Pronobis, KT Schütt, A Tkatchenko, KR Müller The European Physical Journal B 91 (8), 1-6, 2018 | 41 | 2018 |
| XAI for graphs: explaining graph neural network predictions by identifying relevant walks T Schnake, O Eberle, J Lederer, S Nakajima, KT Schütt, KR Müller, ... arXiv e-prints, arXiv: 2006.03589, 2020 | 39* | 2020 |
| Early Detection of Malicious Behavior in JavaScript Code K Schütt, M Kloft, A Bikadorov, K Rieck Proceedings of the 5th ACM workshop on Security and artificial intelligence …, 2012 | 35 | 2012 |
| Generating equilibrium molecules with deep neural networks NWA Gebauer, M Gastegger, KT Schütt NeurIPS Workshop on Machine Learning for Molecules and Materials, 2018 | 25 | 2018 |
| Equivariant message passing for the prediction of tensorial properties and molecular spectra KT Schütt, OT Unke, M Gastegger Proceedings of the 38th International Conference on Machine Learning 139 …, 2021 | 22 | 2021 |
| Quantum-chemical insights from interpretable atomistic neural networks KT Schütt, M Gastegger, A Tkatchenko, KR Müller Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 311-330, 2019 | 22 | 2019 |
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
