Kristof Schütt

Technische Universität Berlin, Berlin, Germany

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Papers24 papers

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Machine learning of solvent effects on molecular spectra and reactions

Michael Gastegger,Kristof T. Schütt,Klaus-Robert Müller

Bibtex

Machine Learning Force Fields

Oliver T. Unke, Stefan Chmiela,Huziel E. Sauceda, Michael Gastegger,Igor Poltavsky,Kristof T. Schütt,Alexandre Tkatchenko,Klaus-Robert Müller

Bibtex

XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks

Schnake Thomas, Eberle Oliver, Lederer Jonas, Nakajima Shinichi,Schütt Kristof T.,Müller Klaus-Robert,Montavon Grégoire

Bibtex

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

Gastegger M., McSloy A., Luya M.,Schütt K. T., Maurer R. J.

The Journal of chemical physics, no. 4 (2020): 044123

WOSBibtex

Autonomous robotic nanofabrication with reinforcement learning

Leinen Philipp, Esders Malte,Schütt Kristof T., Wagner Christian, Müller Klaus-Robert,Tautz F. Stefan

Science advances, no. 36 (2020)

WOSBibtex

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

K T Schütt, M Gastegger,A Tkatchenko,K-R Müller,R J Maurer

Nature communications, no. 1 (2019): 5024-10

WOSBibtex

iNNvestigate neural networks

Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele,Kristof T. Schütt,Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Sven Dähne,Pieter-Jan Kindermans

Journal of Machine Learning Research, (2019)

Cited by59EIBibtex

Learning how to explain neural networks: PatternNet and PatternAttribution

Pieter-Jan Kindermans,Kristof T. Schütt, Maximilian Alber,Klaus-Robert Müller,Dumitru Erhan,Been Kim,Sven Dähne

international conference on learning representations, (2018)

Cited by157EIBibtex

Quantum-chemical insights from interpretable atomistic neural networks

Kristof T. Schütt, Michael Gastegger,Alexandre Tkatchenko,Klaus-Robert Müller

arXiv: Computational Physics, (2018): 311-330

EIBibtex

The (Un)reliability of saliency methods

Pieter-Jan Kindermans, Sara Hooker,Julius Adebayo, Maximilian Alber,Kristof T. Schütt,Sven Dähne,Dumitru Erhan,Been Kim

arXiv: Machine Learning, (2018): 267-280

Cited by131EIBibtex

Generating equilibrium molecules with deep neural networks

Niklas W. A. Gebauer, Michael Gastegger,Kristof T. Schütt

arXiv: Machine Learning, (2018)

EIBibtex

SchNet - A deep learning architecture for molecules and materials

K T Schütt,H E Sauceda,P-J Kindermans,A Tkatchenko,K-R Müller

The Journal of chemical physics, no. 24 (2018): 241722

Cited by19WOSBibtex

Analysis of Atomistic Representations Using Weighted Skip-Connections

Kim A. Nicoli, Pan Kessel, Michael Gastegger,Kristof T. Schütt

arXiv: Computational Physics, (2018)

Bibtex

Learning representations of molecules and materials with atomistic neural networks

Kristof T. Schütt,Alexandre Tkatchenko,Klaus-Robert Müller

arXiv: Computational Physics, (2018)

Cited by2EIBibtex

Capturing intensive and extensive DFT/TDDFT molecular properties with machine learning

Wiktor Pronobis,Kristof T. Schütt,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller

European Physical Journal B, no. 8 (2018): 178

Cited by1Bibtex

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

K T Schütt, P Kessel, M Gastegger, K A Nicoli,A Tkatchenko,K-R Müller

Journal of chemical theory and computation, (2018)

Cited by1WOSBibtex

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela,Klaus-Robert Müller,Klaus-Robert Müller,Alexandre Tkatchenko,Alexandre Tkatchenko

Nature Communications, (2017): 13890

Cited by518Bibtex

An Empirical Study on The Properties of Random Bases for Kernel Methods

Maximilian Alber,Pieter-Jan Kindermans,Kristof Schütt,Klaus-Robert Müller,Fei Sha

NIPS, pp.2760-2771, (2017)

Cited by7EIBibtex

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Kristof Schütt,Pieter-Jan Kindermans,Huziel Enoc Sauceda Felix, Stefan Chmiela,Alexandre Tkatchenko,Klaus-Robert Müller

NIPS, pp.992-1002, (2017)

Cited by161EIBibtex

PatternNet and PatternLRP - Improving the interpretability of neural networks

Pieter-Jan Kindermans,Kristof T. Schütt, Maximilian Alber,Klaus-Robert Müller,Sven Dähne

arXiv: Machine Learning, (2017)

Cited by13EIBibtex

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Kristof SchüttBinding

Papers24 papers

Kristof Schütt