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This paper introduces a novel framework that integrates a Gaussian process model with an adaptive, uncertainty-driven sampling scheme to enhance the sampling of free energy landscapes in molecular simulations. By focusing on regions of high uncertainty, the method iteratively improves the estimation of the free energy landscape while simultaneously using uncertainty as a convergence metric. The results demonstrate that this approach significantly enhances the efficiency of sampling statistically rare events, making it applicable to complex molecular processes.
Uncertainty-driven sampling can dramatically accelerate the exploration of free energy landscapes, transforming how we study rare molecular events.
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.