2.3k citations · 9.1k across the 23 of their papers we have counts for
26 papers
Accelerating crystal structure search through active learning with neural networks for rapid relaxations
Stefaan S. P. Hessmann, Kristof T. Schütt, Niklas W. A. Gebauer +3
Global optimization of crystal compositions is a significant yet computationally intensive method to identify stable structures within chemical space. The specific physical propert…
PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling
Julian Cremer, Tuan Le, Frank Noé +2
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. H…
Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation
Tuan Le, Julian Cremer, Frank Noé +2
Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery. However, their utility is still limited by suboptim…
SchNetPack 2.0: A neural network toolbox for atomistic machine learning
Kristof T. Schütt, Stefaan S. P. Hessmann, Niklas W. A. Gebauer +2
SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with a…
Automatic Identification of Chemical Moieties
Jonas Lederer, Michael Gastegger, Kristof T. Schütt +3
In recent years, the prediction of quantum mechanical observables with machine learning methods has become increasingly popular. Message-passing neural networks (MPNNs) solve this…
Inverse design of 3d molecular structures with conditional generative neural networks
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann +2
The rational design of molecules with desired properties is a long-standing challenge in chemistry. Generative neural networks have emerged as a powerful approach to sample novel m…
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
Oliver T. Unke, Stefan Chmiela, Michael Gastegger +3
Machine-learned force fields (ML-FFs) combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current ML-FFs typically ignore electroni…
Perspective on integrating machine learning into computational chemistry and materials science
Julia Westermayr, Michael Gastegger, Kristof T. Schütt +1
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly establish…
Equivariant message passing for the prediction of tensorial properties and molecular spectra
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 dynamic…
Machine learning of solvent effects on molecular spectra and reactions
Michael Gastegger, Kristof T. Schütt, Klaus-Robert Müller
Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning…
Machine Learning Force Fields
Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda +5
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 traditiona…
A deep neural network for molecular wave functions in quasi-atomic minimal basis representation
M. Gastegger, A. McSloy, M. Luya +2
The emergence of machine learning methods in quantum chemistry provides new methods to revisit an old problem: Can the predictive accuracy of electronic structure calculations be d…
Autonomous robotic nanofabrication with reinforcement learning
Philipp Leinen, Malte Esders, Kristof T. Schütt +3
The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembl…
Unifying machine learning and quantum chemistry -- a deep neural network for molecular wavefunctions
K. T. Schütt, M. Gastegger, A. Tkatchenko +2
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fa…
Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
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. As an exhaustive explor…
Learning representations of molecules and materials with atomistic neural networks
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. In this chapter, we present neural network architectures that are…
Generating equilibrium molecules with deep neural networks
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. Here we introduce a novel, autoregressive, convolutional deep neura…
SchNetPack: A Deep Learning Toolbox For Atomistic Systems
K. T. Schütt, P. Kessel, M. Gastegger +3
SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecule…
iNNvestigate neural networks!
Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer +7
In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. The…
Quantum-chemical insights from interpretable atomistic neural networks
Kristof T. Schütt, Michael Gastegger, Alexandre Tkatchenko +1
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 proper…
SchNet - a deep learning architecture for molecules and materials
Kristof T. Schütt, Huziel E. Sauceda, Pieter-Jan Kindermans +2
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 ch…
The (Un)reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo +5
Saliency methods aim to explain the predictions of deep neural networks. These methods lack reliability when the explanation is sensitive to factors that do not contribute to the m…
Learning how to explain neural networks: PatternNet and PatternAttribution
Pieter-Jan Kindermans, Kristof T. Schütt, Maximilian Alber +4
DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a line…
Investigating the influence of noise and distractors on the interpretation of neural networks
Pieter-Jan Kindermans, Kristof Schütt, Klaus-Robert Müller +1
Understanding neural networks is becoming increasingly important. Over the last few years different types of visualisation and explanation methods have been proposed. However, none…