Exploration vs Convergence Speed in Adaptive-bias Enhanced Sampling

TL;DR

This paper investigates the tradeoff between exploration and convergence speed in adaptive-bias enhanced sampling methods, introducing a new OPES variant that prioritizes rapid metastable state escape over convergence.

Contribution

We propose a new variant of the OPES method that emphasizes quick exploration of phase space, addressing the exploration-convergence tradeoff in adaptive sampling.

Findings

New OPES variant outperforms metadynamics in escaping metastable states

Tradeoff identified between convergence speed and exploration efficiency

Method demonstrated on prototypical systems

Abstract

In adaptive-bias enhanced sampling methods, a bias potential is added to the system to drive transitions between metastable states. The bias potential is a function of a few collective variables and is gradually modified according to the underlying free energy surface. We show that when the collective variables are suboptimal, there is an exploration-convergence tradeoff, and one must choose between a quickly converging bias that will lead to fewer transitions, or a slower to converge bias that can explore the phase space more efficiently but might require a much longer time to produce an accurate free energy estimate. The recently proposed On-the-fly Probability Enhanced Sampling (OPES) method focuses on fast convergence, but there are cases where fast exploration is preferred instead. For this reason, we introduce a new variant of the OPES method that focuses on quickly escaping metastable states, at the expense of convergence speed. We illustrate the benefits of this approach on prototypical systems and show that it outperforms the popular metadynamics method.