Machine Learning for Molecular Science
Cecilia Clementi
2021 Invited Talk
Abstract
I present an overview of the different ways machine learning is making an impact in molecular science. In particular I focus on theoretical and computational biophysics at the molecular scale, and how machine learning is revolutionizing molecular simulation techniques. I present some of the methods developed in the last few years, the results that have been obtained and the challenges ahead. I describe in some detail the application of machine learning to the development of molecular models for biological macromolecules at resolutions coarser than atomistic, that can accurately reproduce the behavior of the system as described by atomistic models or experimental measurements.
Speaker
Cecilia Clementi
Cecilia Clementi is a Professor of Chemistry, and Chemical and
Biomolecular Engineering, and Senior Scientist in the Center for
Theoretical Biological Physics at Rice University. Cecilia received her
Laurea (B.S.) degree in Physics from the University of Florence, Italy, in
1995, and her PhD in Physics from the International School for Advanced
Studies (SISSA/ISAS) in Trieste, Italy, in 1998. After a postdoctoral
fellowship in the La Jolla Interfaces in Science (LJIS) program at the
University of California San Diego, she joined the Rice faculty in 2001,
where she leads an interdisciplinary group working on multiscale
macromolecular modeling. Cecilia's work has been recognized with multiple
awards, such as the Norman Hackerman Award in Chemical Research of the
Welch Foundation, and the NSF-CAREER award.
Cecilia is serving as co-Director of the NSF-funded Molecular Sciences
Software Institute (MolSSI), where she is responsible for the activities
in biomolecular simulation and in international engagement. She is also an Einstein Visiting Fellow at the Freie Universitat in Berlin, Germany.
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