Speedups in nonequilibrium thermal relaxation: Mpemba and related effects
Bibliographic record
Abstract
Most of our intuition about the behavior of physical systems is shaped by observations at or near thermal equilibrium. However, even a basic phenomenon such as a thermalquench can lead to states far from thermal equilibrium, where counterintuitive,anomalous effects can occur. A prime example of anomalous thermal relaxation is the Mpemba effect, in which a system prepared at a hot temperature cools down to the temperature of the cold environment faster than an identical system prepared at a warm temperature. Although originally reported for water more than 2000 years ago by Aristotle, the recent observation of analogous relaxation speedups in a variety of systems has motivated the search for generic explanations from the point of view of nonequilibrium statistical mechanics. Here, we review anomalous relaxation effects, which all share a nonmonotonic dependence of relaxation time versus initial “distance” from the final state or from the phase transition. We review the early water experiments and classify the zoology of anomalous relaxation phenomena related to the Mpemba effect. We then introduce general concepts and provide a modern definition of the effect, focusing on the theoretical frameworks of stochastic thermodynamics, kinetic theory, Markovian dynamics, and phase transitions. We discuss the recent experimental and numerical developments that followed these theoretical advances. These developments paved the way for the prediction and observation of novel phenomena, such as the inverse Mpemba effect. The review is self-contained and introduces anomalous relaxation phenomena in single- and many-body systems, both classical and quantum. We also discuss the broader relevance of the Mpemba effect, including its relation with equilibrium and dynamical phase transitions and its experimental implications. We end with perspectives that connect anomalous speedups to new ideas for designing optimal heating/cooling protocols, heat engines, and efficient samplers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".