Accelerating Molecular Dynamics through Informed Resetting.

We present a procedure for enhanced sampling of molecular dynamics simulations through informed stochastic resetting. Many phenomena, such as protein folding and crystal nucleation, occur over time scales inaccessible in standard simulations. We recently showed that stochastic resetting can accelerate molecular simulations that exhibit broad transition time distributions.

Inference of non-exponential kinetics through stochastic resetting.

We present an inference scheme of long timescale, non-exponential kinetics from molecular dynamics simulations accelerated by stochastic resetting. Standard simulations provide valuable insight into chemical processes but are limited to timescales shorter than ∼1μs. Slower processes require the use of enhanced sampling methods to expedite them and inference schemes to obtain the unbiased kinetics.

Short-Time Infrequent Metadynamics for Improved Kinetics Inference.

Infrequent Metadynamics is a popular method to obtain the rates of long time-scale processes from accelerated simulations. The inference procedure is based on rescaling the first-passage times of the Metadynamics trajectories using a bias-dependent acceleration factor. While useful in many cases, it is limited to Poisson kinetics, and a reliable estimation of the unbiased rate requires slow bias deposition and prior knowledge of efficient collective variables.

The effect of ligands on the size distribution of copper nanoclusters: Insights from molecular dynamics simulations.

Controlling the size distribution in the nucleation of copper particles is crucial for achieving nanocrystals with desired physical and chemical properties. However, their synthesis involves a complex system of solvents, ligands, and copper precursors with intertwining effects on the size of the nanoclusters. We combine molecular dynamics simulations and density functional theory calculations to provide insights into the nucleation mechanism in the presence of a triphenyl phosphite ligand.

Combining stochastic resetting with Metadynamics to speed-up molecular dynamics simulations.

Metadynamics is a powerful method to accelerate molecular dynamics simulations, but its efficiency critically depends on the identification of collective variables that capture the slow modes of the process. Unfortunately, collective variables are usually not known a priori and finding them can be very challenging. We recently presented a collective variables-free approach to enhanced sampling using stochastic resetting.

Stochastic Resetting for Enhanced Sampling.

We present a method for enhanced sampling of molecular dynamics simulations using stochastic resetting. Various phenomena, ranging from crystal nucleation to protein folding, occur on time scales that are unreachable in standard simulations. They are often characterized by broad transition time distributions, in which extremely slow events have a non-negligible probability.