Molecular dynamics saddle search adaptive kinetic Monte Carlo

Area of Science:

• Computational chemistry
• Materials science
• Statistical mechanics

Background:

• Molecular dynamics (MD) simulations are crucial for understanding material properties.
• Simulating rare events in complex systems remains computationally challenging.
• Existing methods often require significant computational resources and time.

Purpose of the Study:

• To develop an efficient method for accelerating molecular dynamics simulations in rare event systems.
• To accurately capture escape mechanisms and rates from system states.
• To provide a reliable estimator for the completeness of calculated rate tables.

Main Methods:

• Utilizing high-temperature MD trajectories to identify escape mechanisms and rates from each visited state.
• Employing an adaptive kinetic Monte Carlo (aKMC) algorithm to model system evolution based on an event table.
• Deriving an estimator to assess the completeness of the computed rate table.

Main Results:

• Successfully applied the method to three distinct model systems: adatom diffusion on Al(100), island diffusion on Pt(111), and vacancy cluster ripening in Fe.
• Demonstrated the ability to accelerate simulations of rare events by efficiently exploring system dynamics.
• Established a framework for estimating the reliability of the generated rate data.

Conclusions:

• The developed method significantly accelerates molecular dynamics simulations for rare event systems.
• The integration of high-temperature MD with aKMC provides a robust approach for modeling complex system evolution.
• The derived completeness estimator enhances the trustworthiness of simulation results.