Machine Learning Complex Dynamics

9:00am - 10:30am
Zoom (Meeting ID: 91294585976 and Passcode: JL0610)

Abstract

Understanding molecular mechanisms requires estimating dynamical statistics such as expected hitting times, reaction rates, and committors. In systems with well-defined metastable states and free energy barriers, these quantities can be estimated using enhanced sampling methods combined with classical rate theories. However, calculating such statistics for more complex processes with rugged landscapes or multiple pathways requires more general numerical methods. In this lecture, the speaker will describe a machine learning framework for calculating dynamical statistics by approximating the dynamical operators of the system through a Galerkin expansion, using statistical estimates from short molecular dynamics trajectories.  It will be demonstrated that this approach gives remarkably accurate results for a well-characterized protein folding reaction with relatively little computational cost.  Finally, the approach will be applied to understanding the dynamics of the protein hormone insulin with a view toward designing improved therapeutics for diabetes.

 

About the speaker

Prof. Aaron R. Dinner obtained his PhD in Biophysics from Harvard University in 1999. He then furthered his postdoctoral research in the University of Oxford and the University of California, Berkeley. In 2003, he joined the University of Chicago as an Assistant Professor and is currently the Professor of Chemistry.

Prof. Dinner and his research group develop theoretical and computational approaches to understand the physical chemical basis of complex behavior in living systems. Their research particularly interested in understanding how cells harness energy from their environments to organize their molecular interactions in space and time. To this end, they are working in close collaboration with experimental researchers to design and analyze quantitative measurements of living systems, and, in turn, implement predictive physical models. One feature of biological dynamics that makes this challenging is that they span a hierarchy of length and time scales ranging from ångstrom and femtoseconds to millimeters and days.

Prof. Dinner was elected a Fellow of the American Physical Society (2016). He also received numerous awards including the Hewlett-Packard Outstanding Junior Faculty Award by the American Chemical Society (2009); the Alfred P. Sloan Fellowship in Chemistry (2008) and the CAREER Award by the US National Science Foundation (2006).



For attendees’ attention
This lecture will be conducted online via Zoom.
https://hkust.zoom.us/j/91294585976 
Passcode: JL0610

When
Time
9:00am - 10:30am
Where
Zoom (Meeting ID: 91294585976 and Passcode: JL0610)
Event Format
Speakers / Performers:
Prof. Aaron R. DINNER
Professor of Chemistry, The University of Chicago
Language
English
Recommended For
Faculty and staff
PG students
UG students
Organizer
HKUST Jockey Club Institute for Advanced Study
Department of Chemistry
School of Science
Contact
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