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Entropy02:39

Entropy

Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
Entropy01:18

Entropy

The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
Entropy and the Second Law of Thermodynamics01:26

Entropy and the Second Law of Thermodynamics

Consider an isolated system in which a hot object is placed in contact with a cold one. This is an irreversible process that eventually leads both objects to reach the same equilibrium temperature. It is crucial to note that the constituents of any substance exhibit increased disorder at higher temperatures. As a cold substance absorbs heat, its constituents become more disordered. The energy transfer from a hotter object to a cooler one increases the system's disorder or randomness. This...
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...

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Related Experiment Video

Updated: Jul 10, 2026

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

Entropic effects in large-scale Monte Carlo simulations.

Cristian Predescu1

  • 1Department of Chemistry and Kenneth S. Pitzer Center for Theoretical Chemistry, University of California, Berkeley, California 94720, USA. cpredescu@comcast.net

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|August 7, 2007
PubMed
Summary

Monte Carlo sampler efficiency depends on energy and entropy. This study quantises entropic divergence using Rényi divergence, crucial for high-dimensional sampling and improving algorithms like smart Monte Carlo (SMC).

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Area of Science:

  • Computational physics
  • Statistical mechanics
  • Algorithm analysis

Background:

  • Monte Carlo samplers are vital for complex systems, but their efficiency is limited by energetic barriers and entropic effects.
  • Entropic effects, specifically the divergence between trial moves, significantly impact sampler performance, especially in high dimensions.

Purpose of the Study:

  • To quantify the impact of entropic divergence on Monte Carlo sampler efficiency.
  • To establish theoretical bounds for acceptance probability using Rényi divergence.
  • To develop improved sampling methods and estimators for complex systems.

Main Methods:

  • Derivation of lower and upper bounds for average acceptance probability using Rényi divergence of order 1/2.
  • Reformulation of acceptance probability as a large deviation problem.
  • Analysis of displacement decay rates for random-walk and smart Monte Carlo (SMC) in high dimensions.
  • Numerical simulations on Lennard-Jones (LJ(38)) clusters.

Main Results:

  • Asymptotic finitude of entropic divergence is necessary and sufficient for nonvanishing acceptance probabilities in high dimensions.
  • The derived upper bound on acceptance probability is asymptotically exact for systems with many independent subsystems.
  • Entropy divergence causes decay in average displacements with increasing dimensions: n(-1/2) for random-walk Monte Carlo and n(-1/6) for SMC.
  • SMC demonstrates efficiency comparable to Gibbs sampling for LJ(38) clusters.
  • Application to parallel tempering shows replica count scales with the square root of heat capacity.

Conclusions:

  • Entropic effects are critical for Monte Carlo sampler efficiency, particularly in high-dimensional spaces.
  • Rényi divergence provides a robust theoretical framework for analyzing and bounding sampler performance.
  • Smart Monte Carlo (SMC) offers a promising, efficient alternative for complex molecular simulations.