Avoiding critical slowdown in models with SALR interactions

Particles with competing short-range attractive and long-range repulsive (SALR) interactions can form a broad array of microphase morphologies. Given that structural richness, minimal models solved by Monte Carlo methods help to hone in on the underlying physics. However, even at weak frustration, configurational sampling in the vicinity of order-disorder transition temperature, $T_c$, is particularly inefficient. Standard cluster algorithms, such as the Swendsen-Wang and Wolff schemes, then fail because they generate clusters that don't capture physical correlations and even percolate at $T>T_c$. Alternate approaches have hence long been sought out. In this presentation, we present a mean-field analysis of this challenge and use our findings to propose an algorithmic approach that sidesteps these difficulties.