IWR Colloquium Winter Semester 2024 / 2025 Generative Machine Learning for Molecular Simulations

Generative machine learning is offering new ways of tackling complex scientific problems. I will describe recent efforts in the context of multiscale molecular simulations of soft-matter systems: a combination of free-energy calculations, representation learning, and Bayesian optimization to design small molecules; a backmapping strategy that learns the atomistic distribution of atoms conditional on the coarse-grained degrees of freedom; and finally a denoising diffusion model to efficiently compute free energies.