Hi! I'm Bálint, I work as an ML researcher at Peptone.

I obtained my doctorate in computer science and physics focusing on generative modeling, sampling and free-energy estimation. My supervisor was François Fleuret. During my PhD I had the chance to intern with the chemistry team of FAIR at Meta working on organic crystal structure prediction; to visit the group of Tristan Bereau where I learnt about free energies; and to spend some time in the AI4Science group of Microsoft Research where I played a small part in their effort to machine learn the exchange-correlation functional.

Prior to starting my PhD, I studied theoretical physics and differential geometry in Hamburg and mechanical engineering in Budapest.



Publications
Solvation Free Energies from Neural Thermodynamic Integration
2025
B. Máté, F. Fleuret and T. Bereau
The Journal of Chemical Physics 162 (12), Editor's Pick
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)
paper | bib