1.6k citations · 5.7k across the 27 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…