1.6k citations · 5.7k across the 27 of their papers we have counts for
29 papers
Relevant Walk Search for Explaining Graph Neural Networks
Ping Xiong, Thomas Schnake, Michael Gastegger +3
Graph Neural Networks (GNNs) have become important machine learning tools for graph analysis, and its explainability is crucial for safety, fairness, and robustness. Layer-wise rel…
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…
Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points
Malte Esders, Gimmy Alex Fernandez Ramirez, Michael Gastegger +1
Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured ap…
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…
Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulations
Oliver T. Unke, Martin Stöhr, Stefan Ganscha +8
Molecular dynamics (MD) simulations allow atomistic insights into chemical and biological processes. Accurate MD simulations require computationally demanding quantum-mechanical ca…
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…
Roaming leads to amino acid photodamage: A deep learning study of tyrosine
Julia Westermayr, Michael Gastegger, Dora Vörös +4
Although the amino acid tyrosine is among the main building blocks of life, its photochemistry is not fully understood. Traditional theoretical simulations are neither accurate eno…
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
Oliver T. Unke, Mihail Bogojeski, Michael Gastegger +3
Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Inst…
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…
Comparing the Accuracy of High-Dimensional Neural Network Potentials and the Systematic Molecular Fragmentation Method: A Benchmark Study for all-trans Alkanes
Michael Gastegger, Clemens Kauffmann, Jörg Behler +1
Many approaches, which have been developed to express the potential energy of large systems, exploit the locality of the atomic interactions. A prominent example are fragmentation…
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng +4
Machine learning models are poised to make a transformative impact on chemical sciences by dramatically accelerating computational algorithms and amplifying insights available from…
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…
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields
Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela +2
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level methods, but at a much lower computational cos…
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…
Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
Julia Westermayr, Michael Gastegger, Philipp Marquetand
In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advan…
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…
Molecular Dynamics with Neural-Network Potentials
Michael Gastegger, Philipp Marquetand
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the…
Analysis of Atomistic Representations Using Weighted Skip-Connections
Kim A. Nicoli, Pan Kessel, Michael Gastegger +1
In this work, we extend the SchNet architecture by using weighted skip connections to assemble the final representation. This enables us to study the relative importance of each in…
Machine learning enables long time scale molecular photodynamics simulations
Julia Westermayr, Michael Gastegger, Maximilian F. S. J. Menger +3
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their appli…