Скачать или смотреть I-AIM Seminar 8 (Yannis Kevrekidis, JHU), Free Energy Surface Exploration, April 02, 2021

Yannis Kevrekidis, Johns Hopkins University
"Manifold Learning for Free Energy Surface Exploration"

Abstract:
Here follows an informal introduction - You have an MD or Monte Carlo code; you believe that there exists a coarse grained, effective free energy surface; and you want to either (a) approximately construct it or (b) perform a task on it, like finding a transition state between two minima. How to do it with the least amount of fine scale/atomistic computation ? I will discuss the equation free/variable free approach to this, that we have been working on since 2000 (see, e.g. [1-3] sampling over the years). It constitutes an integration of "the best atomistic simulation you can do" with manifold learning, numerical analysis and system identification - you all know/recognize the pieces, and the area has been "booming" recently. In particular, I will focus on our recent "intrinsic map dynamics", (iMapD) [4] and its current extensions.

Key references:
​[1] "Coarse molecular dynamics of a peptide fragment: free energy, kinetics and long time dynamics computations"
G. Hummer and I.G.K., J. Chem. Phys. 118(23) pp. 10762-10773 (2003)
[2] "Coarse-grained kinetic computations of rare events: application to micelle formation"
D. I. Kopelevich, A. Z. Panagiotopoulos and I. G. Kevrekidis, J. Chem. Phys. 122 044908 (2005)
[3] "Exploration of effective potential landscapes using coarse reverse integration"
T. A. Frewen, G. Hummer and I. G. Kevrekidis, J. Chem. Phys. 131 134104 (2009)
lots of other "stuff" and then
[4] "iMapD: intrinsic Map Dynamics exploration for uncharted effective free energy landscapes"
E. Chiavazzo, Ronald R. Coifman, Roberto Covino, C. William Gear, Anastasia S. Georgiou, Gerhard Hummer, Ioannis G. Kevrekidis, PNAS 114(28) pp. E5494-5503 (2017)

About the Speaker:
Yannis Kevrekidis is the Bloomberg Distinguished Professor in the Departments of Chemical and Biomolecular Engineering, Department of Applied Mathematics and Statistics, and in the School of Medicine at Johns Hopkins University. He is a member of the National Academy of Arts and Sciences and has been a Packard Fellow, an NSF Presidential Young Investigator, and a Guggenheim Fellow. He holds the Colburn, the Wilhelm, and the Computing in Chemical Engineering awards of the AIChE; the Crawford Prize and the W.T. and Idalia Reid Prize of SIAM; and a Senior Humboldt prize. He has been the Gutzwiller Fellow at the Max Planck Institute for the Physics of Complex Systems in Dresden and a Rothschild Distinguished Visitor at the Newton Institute at Cambridge University, and is currently a senior Hans Fischer Fellow at IAS-TUM in Munich and an Einstein Visiting Fellow at FU/Zuse Institut Berlin. In 2015, he was elected a corresponding member of the Academy of Athens. He also holds a career Teaching Award from the School of Engineering at Princeton University.