BAD-NEUS: Rapidly converging trajectory stratification

TL;DR

This paper introduces BAD-NEUS, an advanced trajectory stratification method that accelerates convergence in molecular dynamics simulations, significantly reducing computational time for long-timescale event sampling.

Contribution

The paper develops a generalized NEUS framework that systematically reduces approximation errors, improving upon existing weighted ensemble methods for faster, unbiased simulation convergence.

Findings

Accelerates convergence by orders of magnitude.

Reduces simulation time for desired precision.

Provides a systematic approach to error reduction.

Abstract

An issue for molecular dynamics simulations is that events of interest often involve timescales that are much longer than the simulation time step, which is set by the fastest timescales of the model. Because of this timescale separation, direct simulation of many events is prohibitively computationally costly. This issue can be overcome by aggregating information from many relatively short simulations that sample segments of trajectories involving events of interest. This is the strategy of Markov state models (MSMs) and related approaches, but such methods suffer from approximation error because the variables defining the states generally do not capture the dynamics fully. By contrast, once converged, the weighted ensemble (WE) method aggregates information from trajectory segments so as to yield unbiased estimates of both thermodynamic and kinetic statistics. Unfortunately, errors decay no faster than unbiased simulation in WE as originally formulated and commonly deployed. Here we introduce a theoretical framework for describing WE that shows that introduction of an approximate stationary distribution on top of the stratification, as in nonequilibrium umbrella sampling (NEUS), accelerates convergence. Building on ideas from MSMs and related methods, we generalize the NEUS approach in such a way that the approximation error can be reduced systematically. We show that the improved algorithm can decrease simulation times required to achieve a desired precision by orders of magnitude.