Search Results for author: Kristof T. Schütt

Found 19 papers, 11 papers with code

SchNetPack 2.0: A neural network toolbox for atomistic machine learning

2 code implementations11 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.

Automatic Identification of Chemical Moieties

no code implementations30 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.

Property Prediction

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

no code implementations1 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.

Equivariant message passing for the prediction of tensorial properties and molecular spectra

2 code implementations5 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.

Machine learning of solvent effects on molecular spectra and reactions

1 code implementation28 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.

BIG-bench Machine Learning

Machine Learning Force Fields

no code implementations14 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.

BIG-bench Machine Learning

Higher-Order Explanations of Graph Neural Networks via Relevant Walks

no code implementations5 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.

Image Classification Sentiment Analysis

Autonomous robotic nanofabrication with reinforcement learning

1 code implementation27 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.

reinforcement-learning Reinforcement Learning (RL)

Symmetry-adapted generation of 3d point sets for the targeted discovery of 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.

Learning representations of molecules and materials with atomistic neural networks

no code implementations11 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.

Generating equilibrium molecules with deep neural networks

no code implementations26 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.

Analysis of Atomistic Representations Using Weighted Skip-Connections

no code implementations23 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.

BIG-bench Machine Learning Property Prediction

iNNvestigate neural networks!

1 code implementation13 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.

Interpretable Machine Learning

Quantum-chemical insights from interpretable atomistic neural networks

no code implementations27 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.

SchNet - a deep learning architecture for molecules and materials

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.

Formation Energy Chemical Physics Materials Science

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

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.

Formation Energy Total Energy

Cannot find the paper you are looking for? You can Submit a new open access paper.