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...

Definitely, it would make a great stress test. Feel free to come by anytime.

2) Yet, previous models such as diffusion models are slow to train and use, and not very accurate moreover. Here, flow matching comes into play allowing for a 100-fold speed-up. In combination with recent developments in machine learning potentials, GoFlow furthermore achieves higher accuracies.

1) Given a chemical reaction, can we predict how exactly the atoms move to go from reactants to products? This reaction pathway determines the barrier height and rate of the reaction, but it is utterly expensive to compute using quantum mechanics. Here, generative AI can help.

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...