Stochastic Resetting for Enhanced Sampling

We present a new approach for enhanced sampling of Molecular Dynamics (MD) simulations using stochastic resetting (SR).

MD simulations is a powerful tool, used for the study of physical and chemical systems at the microscopic level. However, due to their atomic resolution, MD simulations are limited to processes shorter than a few microseconds. Longer processes, such as protein folding, or crystal nucleation and growth, cannot be sampled by standard MD simulations.

SR is the procedure of stopping random processes and restarting them, resampling independent and identically distributed initial conditions. It was shown to expedite different kinds of stochastic processes, ranging from queuing systems to diffusion of colloidal particles. Here, we employ it to enhance MD simulations for the first time, leading to speedups of up to an order of magnitude. We also present an inference procedure, to obtain the unbiased kinetics from simulations with SR.

Next, we demonstrate that SR can be combined with existing enhanced sampling methods, such as Metadynamics (MetaD). For a simple model system, we show that this combination may lead to higher speedups than either approach independently. In another model, we show that restarting MetaD simulations with sub-optimal collective variables (CVs) gives comparable accelerations to using the optimal CV, suggesting resetting can be an easy alternative to improving CVs. We apply the combined approach to alanine tetrapeptide, showing that SR expedites simulations with bad CVs by a factor of 150. Lastly, we present an inference procedure of unbiased kinetics for the combined MetaD and SR method.