203 citations · 461 across the 14 of their papers we have counts for
4 papers · 1 filter
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…
Explaining Bayesian Neural Networks
Kirill Bykov, Marina M. -C. Höhne, Adelaida Creosteanu +4
To advance the transparency of learning machines such as Deep Neural Networks (DNNs), the field of Explainable AI (XAI) was established to provide interpretations of DNNs' predicti…
Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization
Léo Andeol, Yusei Kawakami, Yuichiro Wada +3
Domain shifts in the training data are common in practical applications of machine learning; they occur for instance when the data is coming from different sources. Ideally, a ML m…
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…