no code implementations • 23 May 2024 • Julian Cremer, Tuan Le, Frank Noé, Djork-Arné Clevert, Kristof T. Schütt
The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design.
2 code implementations • 11 Dec 2022 • Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer, Jonas Lederer, Michael Gastegger
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning.
no code implementations • 30 Mar 2022 • Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular.
1 code implementation • 10 Sep 2021 • Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, Kristof T. Schütt
The rational design of molecules with desired properties is a long-standing challenge in chemistry.
no code implementations • 1 May 2021 • Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields.
2 code implementations • 5 Feb 2021 • Kristof T. Schütt, Oliver T. Unke, Michael Gastegger
Message passing neural networks have become a method of choice for learning on graphs, in particular the prediction of chemical properties and the acceleration of molecular dynamics studies.
Ranked #6 on
Drug Discovery
on QM9
1 code implementation • 28 Oct 2020 • Michael Gastegger, Kristof T. Schütt, Klaus-Robert Müller
We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction.
no code implementations • 14 Oct 2020 • Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods.
no code implementations • 5 Jun 2020 • Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T. Schütt, Klaus-Robert Müller, Grégoire Montavon
In this paper, we show that GNNs can in fact be naturally explained using higher-order expansions, i. e. by identifying groups of edges that jointly contribute to the prediction.
1 code implementation • 27 Feb 2020 • Philipp Leinen, Malte Esders, Kristof T. Schütt, Christian Wagner, Klaus-Robert Müller, F. Stefan Tautz
Here, we present a strategy to work around both obstacles, and demonstrate autonomous robotic nanofabrication by manipulating single molecules.
1 code implementation • NeurIPS 2019 • Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials.
no code implementations • 11 Dec 2018 • Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio.
no code implementations • 26 Oct 2018 • Niklas W. A. Gebauer, Michael Gastegger, Kristof T. Schütt
Discovery of atomistic systems with desirable properties is a major challenge in chemistry and material science.
no code implementations • 23 Oct 2018 • Kim A. Nicoli, Pan Kessel, Michael Gastegger, Kristof T. Schütt
In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation.
1 code implementation • 13 Aug 2018 • Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans
The presented library iNNvestigate addresses this by providing a common interface and out-of-the- box implementation for many analysis methods, including the reference implementation for PatternNet and PatternAttribution as well as for LRP-methods.
no code implementations • 27 Jun 2018 • Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko, Klaus-Robert Müller
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers.
5 code implementations • J. Chem. Phys. 2017 • Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans, Alexandre Tkatchenko, Klaus-Robert Müller
Deep learning has led to a paradigm shift in artificial intelligence, including web, text and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Ranked #7 on
Formation Energy
on Materials Project
Formation Energy
Chemical Physics
Materials Science
1 code implementation • ICLR 2018 • Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
Saliency methods aim to explain the predictions of deep neural networks.
5 code implementations • NeurIPS 2017 • Kristof T. Schütt, Pieter-Jan Kindermans, Huziel E. Sauceda, Stefan Chmiela, Alexandre Tkatchenko, Klaus-Robert Müller
Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space.
Ranked #1 on
Time Series
on QM9
4 code implementations • ICLR 2018 • Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, Sven Dähne
We show that these methods do not produce the theoretically correct explanation for a linear model.