Martin Uhrin

Hi, I’m Martin. #

I’m a physicist by training and a computer scientist by heart. I’ve always been fascinated by the “hidden” rules of the universe—the way a few fundamental symmetries dictate the complex behavior of everything around us. My work lives at the intersection of these two worlds, where I develop the computational tools we need to decode and predict the language of atoms.

The Quest for Physical Truth in AI #

In recent years, Machine Learning has taken the world by storm, but “standard” AI often ignores the laws of physics. My passion is in Physics-Informed ML. I don’t just want models that find patterns; I want models that understand the physical world.

I lead the Computational Atomistic Methods and Machine Learning (CAMML) group, where we are particularly obsessed with Equivariant Neural Networks. By baking physical symmetries - like rotations and translations - directly into the architecture of a neural network, we create AI that is not only faster and more data-efficient but fundamentally more “truthful” to the systems it describes. I’m proud to be an active part of the e3nn community, pushing the boundaries of what’s possible when deep learning respects the laws of nature.

Science for the Great Transition #

Beyond the elegance of the math, I am deeply motivated by the Green Transition. Materials science is the silent engine of the future. Whether it’s discovering the next generation of metal-air batteries or designing more efficient catalysts, we are currently limited by how fast we can explore the vast “materials space.”

In the CAMML lab, we build Generative Models to solve this. Instead of the slow, traditional process of trial and error, we develop AI that can “dream up” entirely new atomic structures with the exact properties needed to tackle the global energy crisis.

Life in Grenoble #

Currently, I am based in the beautiful French Alps at the SIMaP laboratory, where I hold a MIAI Chair. Here, surrounded by some of the world’s most powerful experimental tools (like the ESRF and ILL), I get to bridge the gap between abstract theory and real-world materials innovation.

When I’m not thinking about equivariant layers or crystal structures, you’ll likely find skiing in the mountains, or sailing on the lakes.