Adaptive Inference and Convergence of Free Energy Landscapes Using Non-parametric Bayesian Enhanced Sampling
TL;DR
This work reports the development and integration of a Gaussian process model, adaptive, uncertainty-driven sampling scheme for enhanced sampling, which provides a generalizable strategy that can be extended to complex molecular processes.
Abstract
Enhanced sampling techniques are central to the study of statistically rare events in the computer modeling and simulation of molecular phenomena. In this work, we report the development and integration of a Gaussian process model, adaptive, uncertainty-driven sampling scheme for enhanced sampling. The framework trains a Gaussian process model on an iteratively improving estimate of the free energy landscape. Regions of high uncertainty within the reaction phase space are increasingly sampled, and the uncertainty is computed on-the-fly during free energy reconstruction, serving as a convergence metric. This approach provides a generalizable strategy that can be extended to complex molecular processes.