Kristof T. Schütt
Kristof T. Schütt
Pfizer, Machine Learning Research
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Cited by
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
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 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
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 2021
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
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
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
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
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
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
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
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
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
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
Perspective on integrating machine learning into computational chemistry and materials science
J Westermayr, M Gastegger, KT Schütt, RJ Maurer
The Journal of Chemical Physics 154 (23), 2021
Inverse design of 3d molecular structures with conditional generative neural networks
NWA Gebauer, M Gastegger, SSP Hessmann, KR Müller, KT Schütt
Nature communications 13 (1), 973, 2022
Machine learning meets quantum physics
KT Schütt, S Chmiela, OA Von Lilienfeld, A Tkatchenko, K Tsuda, ...
Lecture Notes in Physics, 2020
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
Roadmap on machine learning in electronic structure
HJ Kulik, T Hammerschmidt, J Schmidt, S Botti, MAL Marques, M Boley, ...
Electronic Structure 4 (2), 023004, 2022
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