Štěpán Sršeň

Machine learning for nonadiabatic molecular dynamics: best practices and recent progress Exploring molecular excited states holds immense significance across organic chemistry, chemical biology, and materials science. Understanding the photophysical properties of molecular chromophores is...

Check out our new collaborative paper on machine learning for nonadiabatic molecular dynamics published in @chemicalscience.rsc.org. It provides an overview of the current state-of-the-art and best practices. Thx to
@jwestermayr.bsky.social @stevenalopez.bsky.social @rcrespootero.bsky.social ...

I am happy to share that I am starting a new role as an Assistant Professor at the University of Chemistry and Technology in Prague (@vschtpraha.bsky.social), at the Department of Mathematics, Informatics and Cybernetics. I will further focus on entangling quantum chemistry with machine learning.