A variational conformational dynamics approach to the selection of collective variables in metadynamics

Abstract

In this paper we combine two powerful computational techniques, well-tempered metadynamics and time lagged independent component analysis. The aim is to develop a new tool for studying rare events and exploring complex free energy landscapes. Metadynamics is a well-established and widely used enhanced sampling method whose efficiency depends on an appropriate choice of collective variables. Often the initial choice is not optimal leading to slow convergence. However by analyzing the dynamics generated in one such a run with a time-lagged independent component analysis and the techniques recently developed in the area of conformational dynamics, we obtain much more efficient collective variables, that are also better capable of illuminating the physics of the system. We demonstrate the power of this approach in two paradigmatic examples.

7 pages, 4 figures

References in corpus (4)

• Canonical sampling through velocity-rescaling
• Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
• A Variational Approach to Enhanced Sampling and Free Energy Calculations
• Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations

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