Long-time atomistic dynamics through a new self-adaptive accelerated molecular dynamics method.

Area of Science:

• Materials Science
• Computational Chemistry
• Condensed Matter Physics

Background:

• Modeling infrequent atomic-scale events is computationally challenging.
• Rugged free-energy surfaces hinder traditional molecular dynamics simulations.
• Understanding defect dynamics in materials like iron is crucial for performance and safety.

Purpose of the Study:

• To develop a novel accelerated molecular dynamics method for simulating rare events.
• To investigate the migration and growth mechanisms of helium-vacancy (He-V) clusters in iron.
• To explore coupled atomic motions across different timescales.

Main Methods:

• Developed a self-adaptive accelerated molecular dynamics (MD) method.
• Constructed a boost-potential using system displacement and temperature.
• Applied the method to study He-V cluster dynamics in iron over seconds.

Main Results:

• Simulated migration of single vacancies (V) and small He-V clusters.
• Observed the growth of nano-sized He-V clusters in iron.
• Identified an interstitial-assisted mechanism for He-V cluster migration.
• Proposed a novel two-component Ostwald ripening mechanism for He-V cluster growth.

Conclusions:

• The self-adaptive accelerated MD method effectively models infrequent atomic events.
• New insights into He-V cluster migration and growth mechanisms in iron were obtained.
• The method enables exploration of complex atomic dynamics on disparate timescales.