M. UHRIN Martin
I have a mixed physics and computer science background and love to work at the intersection of the two. Of the things I’ve worked on the common theme is development of computational and mathematical methods that help us to understand, and ultimately predict, atomistic structures (be it via empirical potentials, structure prediction, rationalisation, data mining or other means).
The methods I develop are fairly application agnostic, but one of the things that motivates me is contributing to solutions that drive our green transition, particularly energy materials where I think materials science can make a big contribution. Recently, I was fortunate enough to do a postdoc with Jin Chang and Tejs Vegge at DTU doing research on metal-air batteries.
The current focus of my research is on the development of generative models that, in some sense, understand the structure/property relationship so well that they can propose brand new atomic structures likely to have some set of desired properties. An important component to this is the use of equivariant neural networks that allow us to perform deep-learning while obeying the symmetries of the relevant physical laws. I’m super happy to be working with the talented e3nn team to bring this powerful technology into my research.
Activités / CV
Martin Uhrin holds a master's degree in computational physics from the University of Edinburgh (2009), and a PhD in computational condensed matter physics from University College London (2015). He moved to EPFL, Switzerland as a postdoctoral fellow, where he focused on high-throughput screening for accelerated materials discovery and became the lead author of the workflow engine powering AiiDA, a widely used materials informatics platform. In 2019, he moved to the Danish Technical University to develop machine learning methods for the computational design of battery materials. In 2021, he returned to EPFL as a scientist hosted in the group of Nicola Marzari where he carried out independent research on physics-inspired machine learning methods and inverse design of materials and molecules. Most recently, he was awarded an international chair from the Multidisciplinary Institute in Artificial Intelligence at the University of Grenoble where he started his own team in November 2023.