# Langevin thermostat for robust configurational and kinetic sampling

## TL;DR

This paper reformulates the GJF Langevin thermostat algorithm as a leap frog scheme, enhancing its robustness and accuracy in configurational and kinetic sampling for molecular dynamics, especially at larger time steps.

## Contribution

It introduces a new leap frog formulation of the GJF algorithm that improves kinetic and configurational sampling accuracy and robustness in Langevin dynamics simulations.

## Key findings

- Exact temperature estimation for harmonic oscillators at any stable time step.
- Robust configurational sampling in strongly non-linear systems.
- Enhanced kinetic measure accuracy with the new formulation.

## Abstract

We reformulate the algorithm of Gr{\o}nbech-Jensen and Farago (GJF) for Langevin dynamics simulations at constant temperature. The GJF algorithm has become increasingly popular in molecular dynamics simulations because it provides robust (i.e., insensitive to variations in the time step) and accurate configurational sampling of the phase space with larger time steps than other Langevin thermostats. In the original derivation [Mol. Phys. {\bf 111}, 983 (2013)], the algorithm was formulated as a velocity-Verlet type integrator with an in-site velocity variable. Here, we reformulate it as a leap frog scheme with a half-step velocity variable. In contrast to the original form, the reforumlated one also provides robust and accurate estimations of kinetic measures such as the average kinetic energy. We analytically prove that the newly presented algorithm gives the exact configurational and kinetic temperatures of a harmonic oscillator for any time step smaller than the Verlet stability limit, and use computer simulations to demonstrate the configurational and kinetic robustness of the algorithm in strongly non-linear systems. This property of the new formulation of the GJF thermostat makes it very attractive for implementation in computer simulations.