A global reaction route mapping-based kinetic Monte Carlo algorithm | JoVE Visualize

Area of Science:

Computational Chemistry
Chemical Kinetics
Materials Science

Background:

Accurate simulation of chemical reaction pathways and kinetics is crucial for understanding molecular dynamics and material properties.
Traditional kinetic Monte Carlo (KMC) methods often rely on predefined pathways, limiting their ability to explore complex potential energy surfaces.
Efficient exploration of potential energy surfaces is needed to capture all relevant reaction channels and transition states.

Purpose of the Study:

To develop and validate a novel on-the-fly kinetic Monte Carlo (KMC) method integrated with global reaction route mapping (GRRM).
To enable comprehensive exploration of potential energy surfaces for accurate kinetic simulations.
To demonstrate the method's capability in simulating complex chemical processes.

Main Methods:

The proposed GRRM-KMC algorithm performs an exhaustive search of the potential energy surface starting from an equilibrium state.
It identifies all adjacent transition states using GRRM, calculates intrinsic reaction coordinate pathways to subsequent equilibrium states, and determines rate constants via harmonic transition state theory.
A standard KMC accept/reject step selects the next pathway, propagating the system forward based on first-order kinetics.

Main Results:

The GRRM-KMC algorithm successfully reproduced first-order kinetics in two challenging test cases: intramolecular proton transfer in malonaldehyde and surface carbon diffusion on an iron nanoparticle.
The simulation results aligned with independent quantum chemical molecular dynamics simulations using density-functional tight-binding potentials.
This demonstrates the method's robustness and accuracy in capturing complex kinetic behaviors.

Conclusions:

The GRRM-KMC method provides a powerful and accurate approach for on-the-fly kinetic simulations.
It enables comprehensive exploration of reaction pathways and transition states, overcoming limitations of traditional KMC methods.
This validated algorithm is suitable for studying complex chemical kinetics in various systems, from molecular transfers to surface diffusion.