34 citations · 35 across the 3 of their papers we have counts for
3 papers
Global Concept Explanations for Graphs by Contrastive Learning
Jonas Teufel, Pascal Friederich
Beyond improving trust and validating model fairness, xAI practices also have the potential to recover valuable scientific insights in application domains where little to no prior…
What is missing in autonomous discovery: Open challenges for the community
Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird +16
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has give…
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard +8
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design n…