September 21, 2021
162 Fifth Avenue
America/New_York timezone

Mark E. Tuckerman, Ph.D.,
Professor of Chemistry and Mathematics 
Dept. of Chemistry and Courant Institute of Mathematical Sciences
New York University

Interfacing temperature-accelerated molecular dynamics with machine learning for generating, representing, and deploying high-dimensional free-energy landscapes

The topic will focus on interfacing temperature-accelerated molecular dynamics for enhanced sampling with machine learning for representing high-dimensional free-energy landscapes and deploying them for the calculation of observables.  A comparison of different types of machine learning models, including kernel methods, neural networks, and weighted neighbor methods will be presented.  Under this topic, I also expect to discuss machine learning strategies for learning reaction coordinates from these high-dimensional landscapes.  


162 Fifth Avenue
3rd Floor Classroom / 7th Floor Classroom