Bálint Máté
I am a final yer PhD student in the Computer Science and Physics departments of the University of Geneva under the supervision of François Fleuret. During my PhD I focused on generative modelling, sampling and free energy estimation. Generally speaking, I am interested in machine learning and its applications in the natural sciences.

I am currently interning in the Chemistry team of Meta FAIR and spent the summer of 2023 in the AI4Science group of Microsoft Research in Amsterdam where I played a small part in their effort to machine learn the exchange-correlation (XC) functional. Prior to starting my PhD, I studied theoretical physics and differential geometry in Hamburg and mechanical engineering in Budapest.



I will be graduating in the fall and looking for opportunities primarily in Switzerland. If you are hiring on the interface between machine learning and the natural sciences, please consider reaching out!
Publications
Solvation Free Energies from Neural Thermodynamic Integration
2025
B. Máté, F. Fleuret and T. Bereau
The Journal of Chemical Physics 162 (12)
paper | preprint | summary | bib
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models
2024
B. Máté, F. Fleuret and T. Bereau
The Journal of Physical Chemistry Letters 15 (45)
preprint | paper | summary | bib
Multi-Lattice Sampling of Quantum Field Theories via Neural Operator-based Flows
2024
B. Máté and F. Fleuret
Machine Learning: Science and Technology 5 (4)
paper | bib
Learning Interpolations between Boltzmann Densities
2023
B. Máté and F. Fleuret
Transactions on Machine Learning Research (TMLR)
paper | summary | bib
Flowification: Everything is a Normalizing Flow
2022
B. Máté, S. Klein, T. Golling and F. Fleuret
Neural Information Processing Systems (NeurIPS 2022)
paper | bib