BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Generative Molecular Dynamics - Prof. Simon Olsson\, Chalmers Univ
 ersity of Technology
DTSTART:20260325T153000Z
DTEND:20260325T163000Z
UID:TALK245296@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Molecular dynamics (MD) is an important tool across chemistry\
 , physics\, and biology. MD connects microscopic physics to macroscopic th
 ermodynamic observables yet is often practically limited by the sampling p
 roblem.  Computing thermodynamic observables — free energies and rates -
 -- requires the sampling of statistics from high-dimensional molecular pro
 bability distributions to form unbiased averages and correlations — with
 out a sufficient sample the link is lost. \n \nIn this talk\, I will discu
 ss the advent of Generative Molecular Dynamics [1] as a strategy to effici
 ently generate independent statistics through the training of generative m
 achine learning models. I will outline some of our recent work including i
 mplicit transfer operators [2]\, and our efforts to make this principle ge
 neralize [3\,4].\n \n[1] Olsson “Generative Molecular dynamics” Curren
 t Opinion in Structural Biology 96\, 103213\n[2] Schreiner et al “Implic
 it Transfer Operator Learning: Multiple Time-Resolution Models for Molecul
 ar Dynamics” Advances in Neural Information Processing Systems 36 (NeurI
 PS 2023)\n[3] Diez et al. “Transferable Generative Models Bridge Femtose
 cond to Nanosecond Time-Step Molecular Dynamics” arXiv:2510.07589\n[4] A
 ntoniadis et al. "Protein Language Model Embeddings Improve Generalization
  of Implicit Transfer Operators” arXiv:2602.11216
LOCATION:Pfizer Lecture Theatre - Yusuf Hamied Department of Chemistry
END:VEVENT
END:VCALENDAR