Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.5K
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.
2.5K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.8K
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...
2.8K
Entropy and Solvation02:05

Entropy and Solvation

7.1K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
7.1K
Entropy within the Cell01:22

Entropy within the Cell

10.6K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
10.6K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

83
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
83
Third Law of Thermodynamics02:38

Third Law of Thermodynamics

18.9K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
18.9K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Systematic bottom-up coarse-graining of hydrated excess proton transport across scales.

Nature computational science·2026
Same author

Toward accurate mixed quantum classical simulations of vibrational polaritonic chemistry.

The Journal of chemical physics·2026
Same author

Evaluating Multiconfigurational Trials for Accurate Phaseless Auxiliary-Field Quantum Monte Carlo on 3d Transition Metal Complexes.

Journal of chemical theory and computation·2026
Same author

A bottom-up field-theoretic framework via hierarchical coarse-graining: Generalized mode theory.

The Journal of chemical physics·2026
Same author

Excited states in auxiliary field quantum Monte Carlo.

The Journal of chemical physics·2026
Same author

Chemical Control of Symmetry and Bandgap in Tungsten Oxyhalide van der Waals Semiconductors.

Journal of the American Chemical Society·2025

Related Experiment Video

Updated: Jun 30, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K

Hierarchical Framework for Predicting Entropies in Bottom-Up Coarse-Grained Models.

Jaehyeok Jin1, David R Reichman1

  • 1Department of Chemistry, Columbia University, 3000 Broadway, New York, New York 10027, United States.

The Journal of Physical Chemistry. B
|March 20, 2024
PubMed
Summary

We developed a new framework to predict thermodynamic entropy for coarse-grained (CG) models. This method estimates entropy a priori, reducing computational costs associated with CG simulations.

More Related Videos

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.1K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K

Related Experiment Videos

Last Updated: Jun 30, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
16:41

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

68.7K
Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

3.1K
Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry
12:11

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

8.2K

Area of Science:

  • Computational chemistry
  • Statistical mechanics
  • Molecular modeling

Background:

  • Thermodynamic entropy is crucial for coarse-grained (CG) models, quantifying information loss and enabling transferable interactions.
  • Calculating this entropy typically requires extensive CG simulations, incurring significant computational expense.

Purpose of the Study:

  • To propose a hierarchical framework for predicting the thermodynamic entropies of molecular CG systems.
  • To develop a method that estimates CG thermodynamic properties a priori, bypassing the need for additional CG simulations.

Main Methods:

  • Decomposition of CG interactions to estimate the CG partition function and thermodynamic properties.
  • Application of classical perturbation theory, starting from ideal gas, incorporating hard sphere and generalized van der Waals models.
  • Alternative approach utilizing multiparticle correlation functions for systematic improvements via higher-order correlations.

Main Results:

  • The proposed framework accurately predicts thermodynamic entropies for molecular CG systems.
  • Computational protocols demonstrate that a reduced model with simplified energetics can reliably estimate CG model entropy without CG simulations.
  • The fidelity of the approach was validated through numerical applications to molecular liquids.

Conclusions:

  • A systematic framework is presented for estimating thermodynamic entropy and other properties of CG models.
  • The method relies solely on information from the reference system, offering a computationally efficient alternative.
  • This approach significantly reduces the computational overhead typically associated with determining CG model properties.