Several methods have been used to investigate the evolution of complex systems in conditions where atomistic simulations cannot be performed in reasonable time. Ford et al. developed a method based on a nanoreactor that pushes molecules together so they react more often. With this method, reactions are sampled faster; nonetheless, no information about the rate coefficients can be extracted because pressure and temperature are not well-defined. In this work , Ford et al. then use a newly developed algorithm, the quickest diffusion path, to obtain the transition path and then the reaction rate coefficients. With the kinetic model obtained they solve the chemical master equation using an ordinary differential equation (ODE) solver. As this method relies on Transition State Theory for gas phase, it cannot be applied to dense phase, where a widely-accepted Transition State Theory framework has not been developed yet. Another approach to circumvent the short timescale of atomistic simulations is to simply increase the temperature of the system, because at higher temperatures, chemical reactions occur more often leading to shorter equilibration times. This approach has been used to study hydrocarbon pyrolysis , hydrogen combustion , methane oxidation , and hydrogen peroxide decomposition . However, this method can only be applied above a certain temperature, where the equilibration time is considered reasonably small to be run in an MD simulation. As some applications may need simulations that reach equilibrium at lower temperatures, there is a need for a method to accurately simulate the evolution of the system in these conditions.