203 citations · 406 across the 7 of their papers we have counts for
7 papers
Generative Pseudo-Force Fields for Molecular Generation
Stefaan Simon Pierre Hessmann, Khaled Kahouli, Stefan Gugler +4
Generating stable molecular conformations typically forces a tradeoff between the physical realism of energy-based relaxation and the sampling efficiency of data-driven generative…
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…
Molecular relaxation by reverse diffusion with time step prediction
Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller +3
Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force f…
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…
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…
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…
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…