Seminar über Theorie der Kondensierten Materie/ Weiche Materie und Statistische Physik

Programm für das Wintersemester 2022/2023

Thursdays, 10:30 Uhr s.t.

K. Binder/ A. Nikoubashman / F. Schmid / G. Settanni / T. Speck / M. Sulpizi / P. Virnau
Newtonraum, 01-122, Staudingerweg 9

01.12.22Oliver Beckstein, Arizona State University
Multiscale modelling of transmembrane transport processes with kinetic cycle models
15:00 Uhr s.t., Remote

12.12.22Wanda Niemyska, University of Warsaw
New knots found in human proteome based on AlphaFold predictions
15:30 Uhr s.t.

22.12.22Thomas Mehren, Physiker
18:00 Uhr s.t., Minkowski-Raum 05-119, Staudingerweg 7

20.01.23Roland Netz, Freie Universität Berlin
10:00 Uhr s.t., Newtonraum, 01-122, Staudingerweg 9

26.01.23Andrey Milchev, Institute of Physical Chemistry, Bulgarian Academy of Sciences
Translocation Dynamics of Vesicles Through Narrow Pores
11:30 Uhr s.t., Hörsaal Kernphysik

03.02.23Jeetain Mittal, Texas A&M
10:00 Uhr s.t., Newtonraum, 01-122, Staudingerweg 9

09.02.23Roberto Covino, Frankfurt Institute for Advanced Studies
Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Understanding the physical mechanisms that govern the formation of molecular complexes is key to controlling the assembly of nanomachines and new materials. Molecular dynamics simulations and single-molecule experiments offer the unprecedented possibility to reveal mechanisms of molecular self-organization in high resolution. However, outstanding limitations hinder their success. Machine learning and artificial intelligence promise to empower both approaches to overcome fundamental challenges. In the first part of my talk, I will present an autonomous AI that learns molecular mechanisms from computer simulations. The AI agent simulates infrequent and stochastic molecular reorganizations and progressively learns how to predict their outcome. Using symbolic regression, we distill simplified quantitative models that reveal mechanistic insight in a human-understandable form. Our innovative AI enables sampling rare events by autonomously driving many parallel simulations with minimal human intervention and aids their mechanistic interpretation. I will present applications on nucleation processes, the assembly of membrane proteins in lipid bilayers, and polymer and protein folding. In the second part of my talk, I will discuss how integrating physical modeling and AI helps extract mechanistic understanding from single-molecule force spectroscopy. While these experiments offer the possibility of measuring fundamental quantities like free energies, these measurements are often incomplete and indirect. In practice, we measure a few order parameters that are the outcome of the coupled dynamics of the molecule and the mesoscopic experimental apparatus, which could lead to estimation artifacts. I will discuss this problem as Bayesian inference and illustrate how simulation-based inference provides a powerful solution. Coupling a simulator that encodes the physics of the measuring process with density estimation using neural networks leads to accurate estimates of molecular free energies. In conclusion, integrating physics-based models and AI provides a powerful way to extract accurate quantitative information from simulations and biophysical experiments.
15:00 Uhr s.t., Minkowski Room, 05-119, Staudingerweg 7,

Koordination: Kontakt:

K. Binder

A. Nikoubashman

F. Schmid

G. Settanni

T. Speck

M. Sulpizi

P. Virnau

L. Stelzl

Lukas Stelzl