Photo: © Ulrich Zinell
Heid Lab · TU Wien · Institute of Materials Chemistry
Deep learning for chemical reactions
Bridging AI and mechanistic chemistry to enable high-throughput screening of chemical reactions.
Photo: © Ulrich Zinell
Heid Lab · TU Wien · Institute of Materials Chemistry
Bridging AI and mechanistic chemistry to enable high-throughput screening of chemical reactions.
What we do
The Heid Lab is led by Esther Heid, Assistant Professor for Machine Learning for Sustainable Chemistry at TU Wien. We develop machine learning methods to predict and model chemical reactions, ranging from barrier heights and reaction energies to transition state geometries. Our work combines graph neural networks, generative models, and quantum chemistry.
Our research spans reaction property prediction, transition state modeling, and ML-accelerated automated reaction pathway search across gas-phase, organocatalytic, and biocatalytic reactions.
Explore our work →Projects
Our work is supported by multiple grants including a highly competitive ERC Starting Grant and an FWF START Prize, supporting the group's long-term research on machine learning for chemical reactions as part of the DeepRxn project.
What we've been up to
Selected work
Here's a selection of recent highlights.
L. Galustian, K. Mark, J. Karwounopoulos, M. P.-P. Kovar, E. Heid
Read paper →J. Karwounopoulos, J. De Landsheere, L. Galustian, T. Jechtl, E. Heid
Read paper →J. De Landsheere, A. Zamyatin, J. Karwounopoulos, E. Heid
Read paper →Contact
Whether you're thinking about joining, looking to collaborate, or just want to exchange ideas — we'd be glad to hear from you.