Bypassing sluggishness: SWAP algorithm and glassiness in high dimensions.

Area of Science:

• Computational physics
• Materials science
• Statistical mechanics

Background:

• Simulating glass-forming mixtures is computationally intensive.
• Existing methods face limitations in bridging experimental and simulation timescales.

Purpose of the Study:

• To evaluate the performance of the swap Monte Carlo algorithm (SWAP) in higher dimensions (d=2-8).
• To understand the underlying physics of SWAP's efficiency across dimensions.
• To assess SWAP's potential for studying glassy dynamics in computationally challenging systems.

Main Methods:

• Implementation of the swap Monte Carlo algorithm (SWAP).
• Simulation of polydisperse glass-forming mixtures in dimensions d=2, 3, ..., 8.
• Analysis of computational speedup and dynamics onset.

Main Results:

• SWAP significantly reduces computational sluggishness in 2 and 3 dimensions.
• The observed speedup diminishes rapidly as dimensionality increases.
• SWAP delays the onset of activated dynamics by a seemingly finite amount even in the limit of infinite dimensions.

Conclusions:

• Glassy dynamics in dimensions d>3 become computationally accessible with SWAP.
• This advancement allows for systematic studies of finite-dimensional deviations from mean-field theory.
• SWAP offers a powerful tool for exploring complex glassy systems across various dimensions.