| Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8 (13890), 2017 | 354 | 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 | 197 | 2014 |
| 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 | 193 | 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 | 126 | 2018 |
| 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 | 101 | 2017 |
| 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 | 92* | 2018 |
| 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 | 76 | 2019 |
| iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... Journal of Machine Learning Research 20 (93), 1-8, 2019 | 23 | 2019 |
| 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 | 23 | 2012 |
| 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 | 20 | 2019 |
| 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 | 20 | 2016 |
| 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), 178, 2018 | 10 | 2018 |
| Quantum-chemical insights from interpretable atomistic neural networks KT Schütt, M Gastegger, A Tkatchenko, KR Müller arXiv preprint arXiv:1806.10349, 2018 | 8 | 2018 |
| Discover significant situations for user interface adaptations SR Garzon, K Schütt Proceedings of the 3rd Workshop on Multimodal Interfaces for Automotive …, 2011 | 4 | 2011 |
| Generating equilibrium molecules with deep neural networks NWA Gebauer, M Gastegger, KT Schütt NeurIPS Workshop on Machine Learning for Molecules and Materials, 2018 | 3 | 2018 |
| An Empirical Study on The Properties of Random Bases for Kernel Methods M Alber, PJ Kindermans, KT Schütt, KR Müller, F Sha Advances in Neural Information Processing System 30, 2760--2771, 2017 | 3 | 2017 |
| Unifying machine learning and quantum chemistry - a deep neural network for molecular wavefunctions KT Schütt, M Gastegger, A Tkatchenko, KR Müller, RJ Maurer arXiv preprint arXiv:1906.10033, 2019 | 2 | 2019 |
| Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules NWA Gebauer, M Gastegger, KT Schütt arXiv preprint arXiv:1906.00957, 2019 | 2 | 2019 |
| Modeling Molecular Spectra with Interpretable Atomistic Neural Networks M Gastegger, KT Schütt, HE Sauceda, KR Müller, A Tkatchenko Bulletin of the American Physical Society, 2019 | 1 | 2019 |
| Learning representations of molecules and materials with atomistic neural networks KT Schütt, A Tkatchenko, KR Müller arXiv preprint arXiv:1812.04690, 2018 | 1 | 2018 |
Klaus-Robert MüllerProfessor for Machine Learning, TU Berlin, Germany and Korea University, Seoul, Korea and MPIIVerified email at tu-berlin.de