A kinetic Monte Carlo approach to diffusion-controlled thermal desorption spectroscopy

Area of Science:

• Materials Science
• Computational Physics
• Chemical Engineering

Background:

• Atomistic simulations for thermal desorption spectra are computationally intensive due to large system size and time scale requirements.
• Characterizing binding/trapping sites in bulk materials necessitates efficient simulation methods.

Purpose of the Study:

• To develop a computationally efficient approach for atomistic simulations of thermal desorption spectra.
• To enable the study of structural influences on desorption peak characteristics.

Main Methods:

• Combined kinetic Monte Carlo with an analytic approximation of superbasins using absorbing Markov chains.
• Applied the method to simulate hydrogen effusion from BCC iron, coarse-graining bulk diffusion dynamics.
• Developed a transferable analytic approximation for superbasins applicable to various grain sizes and shapes.

Main Results:

• Achieved close agreement between simulated and direct kinetic Monte Carlo thermal desorption spectra.
• Demonstrated significant computational efficiency, allowing for larger system sizes.
• Validated the transferability of the analytic superbasin approximation across different grain geometries.

Conclusions:

• The developed method offers a computationally efficient alternative for atomistic simulations of thermal desorption.
• This approach facilitates a deeper understanding of how structural features impact desorption spectra.
• Paves the way for atomistic investigations of hydrogen-metal interactions and material characterization.