Want to steer your flow matching models, not just diffusion? We have just derived Feynman-Kac-Steering for flow matching, eg to steer chemical transition states toward correct chirality. Fantastic work by Konstantin Mark, along with Leonard Galustian and Maximilian Kovar. arxiv.org/abs/2509.01543

Graph neural networks are inherently limited in predicting chemical reaction properties by the absence of 3D information. We showcase how generative models can create guess structures for reaction pathways, and encode these into GNNs for reaction barrier height prediction: doi.org/10.26434/che...

Very proud to introduce the first preprint of the Heid lab on generative AI for chemical reactions, where we combine flow-matching with equivariant neural networks to predict transition state geometries only based on reaction graphs. Read more below or check out the paper: doi.org/10.26434/che...