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Esther Heid @esther-heid.bsky.social · 12/02/2026

Do you love building AI/ML models across a wide range of research topics? TU Wien is hiring two senior scientists/postdocs for a university-wide AI/ML service center (www.tuwien.at/datatudiscovery). Computer science: jobs.tuwien.ac.at/Job/263874 Science/engineering: jobs.tuwien.ac.at/Job/263875

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Esther Heid @esther-heid.bsky.social · 03/09/2025

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

arxiv.org

Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior

Conditional Flow Matching(CFM) represents a fast and high-quality approach to generative modelling, but in many applications it is of interest to steer the generated samples towards precise requiremen...

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Esther Heid @esther-heid.bsky.social · 04/06/2025

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...

doi.org

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states

The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph ne...

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Esther Heid @esther-heid.bsky.social · 07/05/2025

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...

doi.org

GoFlow: Efficient Transition State Geometry Prediction with Flow Matching and E(3)-Equivariant Neural Networks

Transition state (TS) geometries of chemical reactions are key to understanding reaction mechanisms and estimating kinetic properties. Inferring these directly from 2D reaction graphs offers chemists ...

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