Caliber based spectral gap optimization of order parameters (SGOOP) for sampling complex molecular systems

TL;DR

This paper introduces a new algorithm called SGOOP that optimizes low-dimensional collective variables for enhanced sampling in molecular simulations, significantly improving convergence and efficiency even with limited prior information.

Contribution

The paper presents a novel spectral gap optimization method for selecting optimal collective variables in enhanced sampling, applicable with minimal prior knowledge and multiple candidate CVs.

Findings

SGOOP improves convergence of free energy calculations by orders of magnitude.

The method effectively extracts useful information from unsuccessful metadynamics runs.

Practical examples demonstrate the algorithm's ability to optimize CVs and accelerate sampling.

Abstract

In modern day simulations of many-body systems much of the computational complexity is shifted to the identification of slowly changing molecular order parameters called collective variables (CV) or reaction coordinates. A vast array of enhanced sampling methods are based on the identification and biasing of these low-dimensional order parameters, whose fluctuations are important in driving rare events of interest. Here describe a new algorithm for finding optimal low-dimensional collective variables for use in enhanced sampling biasing methods like umbrella sampling, metadynamics and related methods, when limited prior static and dynamic information is known about the system, and a much larger set of candidate CVs is specified. The algorithm involves estimating the best combination of these candidate CVs, as quantified by a maximum path entropy estimate of the spectral gap for dynamics viewed as a function of that CV. Through multiple practical examples, we show how this post-processing procedure can lead to optimization of CV and several orders of magnitude improvement in the convergence of the free energy calculated through metadynamics, essentially giving the ability to extract useful information even from unsuccessful metadynamics runs.