Riemann metric approach to optimal sampling of multidimensional free-energy landscapes

TL;DR

This paper introduces a Riemannian metric for optimal sampling of high-dimensional free-energy landscapes, improving sampling efficiency by accounting for local diffusion and invariance under coordinate transformations.

Contribution

It proposes a novel multidimensional Riemann metric to optimize sampling in free-energy calculations, enhancing efficiency in complex molecular simulations.

Findings

Sampling efficiency improved by 50-70% in DNA base-pair opening simulations.

The metric enables invariant and adaptive sampling without significant computational overhead.

Abstract

Exploring the free-energy landscape along reaction coordinates or system parameters $\lambda$ is central to many studies of high-dimensional model systems in physics, e.g. large molecules or spin glasses. In simulations this usually requires sampling conformational transitions or phase transitions, but efficient sampling is often difficult to attain due to the roughness of the energy landscape. For Boltzmann distributions, crossing rates decrease exponentially with free-energy barrier heights. Thus, exponential acceleration can be achieved in simulations by applying an artificial bias along $\lambda$ tuned such that a flat target distribution is obtained. A flat distribution is however an ambiguous concept unless a proper metric is used, and is generally suboptimal. Here we propose a multidimensional Riemann metric, which takes the local diffusion into account, and redefine uniform sampling such that it is invariant under nonlinear coordinate transformations. We use the metric in combination with the accelerated weight histogram method, a free-energy calculation and sampling method, to adaptively optimize sampling toward the target distribution prescribed by the metric. We demonstrate that for complex problems, such as molecular dynamics simulations of DNA base-pair opening, sampling uniformly according to the metric, which can be calculated without significant computational overhead, improves sampling efficiency by 50-70%.