Michael Plainer

Michael Plainer is a Ph.D. student in computer science at Freie Universität Berlin and the ELIZA Zuse School, working with Frank Noé and Klaus-Robert Müller on AI for Science. His research focuses on generative models for dynamical biophysical systems, including molecular dynamics and diffusion-based methods for biomolecular simulation.

Hamiltonian Flow Maps Learn Larger Molecular Dynamics Steps Without Trajectories

Michael Plainer, Winfried Ripken and Gregor Lied argue that generative models can attack molecular dynamics’ central bottleneck: the gap between femtosecond integration steps and biological processes that unfold many orders of magnitude later. In the Microsoft Research seminar, they separate the problem by timescale, using diffusion models to sample equilibrium Boltzmann states and extract force information, while proposing Hamiltonian flow maps for the intermediate regime where simulations need large, stable steps without training on expensive future-state trajectories.

Microsoft ResearchMay 26, 202618 min read