Kristof T. Schütt

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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Alexandre TkatchenkoProfessor of Physics, University of Luxembourg; Visiting Professor, Berlin Big Data CenterVerified email at uni.lu
Pieter-Jan KindermansSenior Research Scientist, Google BrainVerified email at google.com
Michael GasteggerTU BerlinVerified email at tu-berlin.de
Stefan ChmielaTechnische Universität BerlinVerified email at chmiela.com
Huziel E. SaucedaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Sven DähneAmazonVerified email at amazon.com
Reinhard J. MaurerAssociate Professor, Department of Chemistry, University of WarwickVerified email at warwick.ac.uk
Igor PoltavskyPostDocVerified email at uni.lu
Dumitru ErhanStaff Research Scientist, Google BrainVerified email at google.com
Been KimGoogle BrainVerified email at csail.mit.edu
Farhad ArbabzadahBerlin Institute of TechnologyVerified email at tu-berlin.de
Grégoire MontavonTechnische Universität BerlinVerified email at tu-berlin.de
Wojciech SamekHead of AI Department, Fraunhofer HHI, Germany, BIFOLD Fellow & ELLIS BerlinVerified email at hhi.fraunhofer.de
Philipp SeegererDoctoral Researcher, Machine Learning Group, TU BerlinVerified email at tu-berlin.de
Sebastian Lapuschkin (né Bach)Head of Explainable AI Group, Fraunhofer Heinrich Hertz InstituteVerified email at hhi.fraunhofer.de
Miriam HägeleTechnische Universität BerlinVerified email at tu-berlin.de
E.K.U. GrossThe Hebrew University of Jerusalem eberhard.gross'at'mail.huji.ac.ilVerified email at mail.huji.ac.il
Antonio SannaMax Plank Institute of microstructure physicsVerified email at mpi-halle.mpg.de

Kristof T. Schütt

Technische Universität Berlin

Verified email at tu-berlin.de

machine learning deep learning quantum chemistry


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Quantum-chemical insights from deep tensor neural networks KT Schütt, F Arbabzadah, S Chmiela, KR Müller, A Tkatchenko Nature Communications 8 (13890), 2017	748	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	521	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	498	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	321	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	292	2017
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	259	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	259*	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	144	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	142	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	119	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	76	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), 1-6, 2018	32	2018
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	32	2012
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	31	2019
Machine Learning Meets Quantum Physics KT Schütt, S Chmiela, OA von Lilienfeld, A Tkatchenko, K Tsuda, ... Springer International Publishing, 2020	28	2020
Machine learning force fields OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ... arXiv preprint arXiv:2010.07067, 2020	19	2020
Generating equilibrium molecules with deep neural networks NWA Gebauer, M Gastegger, KT Schütt NeurIPS Workshop on Machine Learning for Molecules and Materials, 2018	18	2018
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	16	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	12	2020
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	11	2017

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