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Updated: Apr 17, 2026

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Experimental Methods for Investigation of Shape Memory Based Elastocaloric Cooling Processes and Model Validation

Published on: May 2, 2016

Annealed importance sampling with constant cooling rate.

Edoardo Giovannelli¹, Gianni Cardini¹, Cristina Gellini¹

¹Dipartimento di Chimica, Università di Firenze, Via della Lastruccia 3, I-50019 Sesto Fiorentino, Italy.

The Journal of Chemical Physics

|February 23, 2015

Summary

This summary is machine-generated.

Annealed importance sampling (AIS) provides a robust method for calculating averages from simulated annealing trajectories. This review connects AIS to nonequilibrium methods and suggests optimal cooling schedules for improved performance.

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Area of Science:

Computational Physics
Statistical Mechanics
Simulation Methods

Background:

Annealed importance sampling (AIS) is a simulation technique developed by Neal for weighting configurations from simulated annealing.
It enables computation of equilibrium averages using weighted averages of final configurations from annealed trajectories.

Purpose of the Study:

To review annealed importance sampling (AIS) from the perspective of nonequilibrium path-ensemble averages.
To demonstrate the generality of AIS beyond thermal protocols.
To identify optimal temperature schedules for enhanced performance.

Main Methods:

Review of existing literature on annealed importance sampling (AIS).
Comparison of AIS with nonequilibrium path-ensemble averages (Crooks' method).
Analysis of different temperature schedules (constant cooling rate vs. stepwise).

Main Results:

Equivalence established between Neal's AIS and Crooks' nonequilibrium treatments.
Constant cooling rate schedules are shown to outperform stepwise cooling.
Increasing intermediate temperatures generally improves AIS performance for a fixed computation time.

Conclusions:

AIS is a versatile method applicable to various protocols, not just thermal ones.
Optimized temperature schedules, particularly constant cooling rates, enhance the efficiency of AIS.
Further improvements in AIS performance can be achieved by increasing the number of intermediate temperatures.