Designing Molecular Models by Machine Learning and Experimental Data
- Starts: 11:00 am on Friday, April 16, 2021
- Ends: 12:00 pm on Friday, April 16, 2021
Speaker: Cecilia Clementi, Professor of Physics & Researcher at Freie Universität Berlin
This event is part of our Distinguished Speaker Series: Machine Learning for Model-Rich Problems.
Abstract: The last years have seen an immense increase in high-throughput and
high-resolution technologies for experimental observation as well as
high-performance techniques to simulate molecular systems at a microscopic
level, resulting in vast and ever-increasing amounts of high-dimensional data.
However, experiments provide only a partial view of macromolecular processes
and are limited in their temporal and spatial resolution. On the other hand,
atomistic simulations are still not able to sample the conformation space of
large complexes, thus leaving significant gaps in our ability to study
molecular processes at a biologically relevant scale. We present our efforts to
bridge these gaps, by exploiting the available data and using state-of-the-art
machine-learning methods to design optimal coarse models for complex
macromolecular systems. We show that it is possible to define simplified
molecular models to reproduce the essential information contained both in
microscopic simulation and experimental measurements.
- Location:
- Zoom Webinar. The Zoom login information can be found on our Eventbrite page, below, after registering.
- Link:
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