Double Mpemba effect in the cooling of trapped colloids.

This article focuses on the investigation of the Mpemba effect—where a system initially at a higher temperature cools faster than one at a lower temperature—in passive colloidal particles confined within an asymmetric double-well potential. Using numerical Brownian dynamics simulations and theoretical modeling based on the Fokker–Planck equation, the study reveals a novel "Double Mpemba effect," characterized by two distinct minima in cooling time as a function of initial temperature. The research further demonstrates that machine-learning algorithms, including Random Forest and Decision Trees, can accurately classify and predict the occurrence and type of Mpemba effect, offering a computationally efficient alternative to traditional methods. These findings extend the understanding of anomalous cooling dynamics in colloidal systems and provide a framework for experimental verification and future exploration in related physical and quantum systems.