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In modern day simulations of many-body systems much of the computationalcomplexity is shifted to the identification of slowly changing molecular orderparameters called collective variables (CV) or reaction coordinates. A vastarray of enhanced sampling methods are based on the identification and biasingof these low-dimensional order parameters, whose fluctuations are important indriving rare events of interest. Here describe a new algorithm for findingoptimal low-dimensional collective variables for use in enhanced samplingbiasing methods like umbrella sampling, metadynamics and related methods, whenlimited prior static and dynamic information is known about the system, and amuch larger set of candidate CVs is specified. The algorithm involvesestimating the best combination of these candidate CVs, as quantified by amaximum path entropy estimate of the spectral gap for dynamics viewed as afunction of that CV. Through multiple practical examples, we show how thispost-processing procedure can lead to optimization of CV and several orders ofmagnitude improvement in the convergence of the free energy calculated throughmetadynamics, essentially giving the ability to extract useful information evenfrom unsuccessful metadynamics runs.

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