Likelihoodbased nonMarkovian models from molecular dynamics
Abstract
The analysis of complex systems with many degrees of freedom generally involves the definition of lowdimensional collective variables more amenable to physical understanding. Their dynamics can be modeled by generalized Langevin equations, whose coefficients have to be estimated from simulations of the initial highdimensional system. These equations feature a memory kernel describing the mutual influence of the lowdimensional variables and their environment. We introduce and implement an approach where the generalized Langevin equation is designed to maximize the statistical likelihood of the observed data. This provides an efficient way to generate reduced models to study dynamical properties of complex processes such as chemical reactions in solution, conformational changes in biomolecules, or phase transitions in condensed matter systems.
 Publication:

Proceedings of the National Academy of Science
 Pub Date:
 March 2022
 DOI:
 10.1073/pnas.2117586119
 arXiv:
 arXiv:2110.04246
 Bibcode:
 2022PNAS..11917586V
 Keywords:

 Condensed Matter  Statistical Mechanics;
 Physics  Chemical Physics;
 Physics  Computational Physics
 EPrint:
 12 pages, 7 figures