Kristof T. Schütt
Kristof T. Schütt
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Zitiert von
Zitiert von
Quantum-chemical insights from deep tensor neural networks
KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko
Nature Communications 8 (13890), 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
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
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
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
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
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
iNNvestigate neural networks!
M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ...
J. Mach. Learn. Res. 20 (93), 1-8, 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
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
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
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
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
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
Machine Learning Meets Quantum Physics
KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ...
Springer International Publishing, 2020
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
Generating equilibrium molecules with deep neural networks
NWA Gebauer, M Gastegger, KT Schütt
NeurIPS Workshop on Machine Learning for Molecules and Materials, 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
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 preprint arXiv:2006.03589, 2020
Learning representations of molecules and materials with atomistic neural networks
KT Schütt, A Tkatchenko, KR Müller
Machine Learning Meets Quantum Physics, 215-230, 2020
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