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Related Concept Videos

Free Energy and Equilibrium00:55

Free Energy and Equilibrium

The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔG is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
The reaction quotient, Q, is a convenient measure of the status of an...
Free Energy and Equilibrium02:56

Free Energy and Equilibrium

The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
Recall that Q is the numerical value of the mass action expression...
Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

The free energy change for a reaction that occurs under the standard conditions of 1 bar pressure and at 298 K is called the standard free energy change. Since free energy is a state function, its value depends only on the conditions of the initial and final states of the system. A convenient and common approach to the calculation of free energy changes for physical and chemical reactions is by use of widely available compilations of standard state thermodynamic data. One method involves the...
Gibbs Free Energy02:39

Gibbs Free Energy

One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
Gibbs Free Energy and Thermodynamic Favorability02:23

Gibbs Free Energy and Thermodynamic Favorability

The spontaneity of a process depends upon the temperature of the system. Phase transitions, for example, will proceed spontaneously in one direction or the other depending upon the temperature of the substance in question. Likewise, some chemical reactions can also exhibit temperature-dependent spontaneities. To illustrate this concept, the equation relating free energy change to the enthalpy and entropy changes for the process is considered:
Potential-Energy Criterion for Equilibrium01:16

Potential-Energy Criterion for Equilibrium

Potential energy or potential function plays an essential role in determining the stability of a mechanical system. If a system is subjected to both gravitational and elastic forces, the potential function of the system can be expressed as the algebraic sum of gravitational and elastic potential energy. If the system is in equilibrium and is displaced by a small amount, then the work done on the system equals the negative of the change in the system's potential energy from the initial to the...

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Related Experiment Video

Updated: Jul 10, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Data-efficient multidimensional free energy estimation via physics-informed score learning.

Daniel Nagel1, Tristan Bereau1,2

  • 1Institute for Theoretical Physics, Heidelberg University, 69120 Heidelberg, Germany.

The Journal of Chemical Physics
|July 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Fokker-Planck Score Learning (FPSL) for efficient two-dimensional free-energy landscape reconstruction. FPSL overcomes computational costs, offering a scalable tool for complex biological systems.

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Published on: January 26, 2024

Area of Science:

  • Computational Chemistry
  • Biophysics
  • Statistical Mechanics

Background:

  • Estimating free-energy landscapes is crucial for understanding biological processes.
  • High computational costs limit current methods to one-dimensional analyses.
  • Multidimensional sampling is computationally intensive, hindering deeper insights.

Purpose of the Study:

  • To extend Fokker-Planck Score Learning (FPSL) for efficient reconstruction of 2D free-energy landscapes.
  • To demonstrate the benefits of modeling orthogonal degrees of freedom in free-energy estimation.
  • To validate the FPSL approach on diverse biological systems.

Main Methods:

  • Utilizing non-equilibrium molecular dynamics simulations.
  • Applying Fokker-Planck Score Learning (FPSL) to reconstruct free-energy landscapes.
  • Incorporating collective variables and exploiting landscape symmetries for enhanced accuracy.
  • Employing regularization techniques for numerical robustness in sparse data regions.

Main Results:

  • Successfully reconstructed 2D free-energy landscapes for alanine dipeptide and solute permeation through lipid bilayers.
  • Demonstrated that modeling orthogonal degrees of freedom provides hidden insights.
  • Showcased FPSL's ability to overcome the exponential scaling of grid-based methods.
  • Validated FPSL's data efficiency and scalability for multidimensional free-energy estimation.

Conclusions:

  • FPSL is a powerful, data-efficient, and scalable tool for multidimensional free-energy estimation.
  • Explicitly modeling multiple degrees of freedom enhances the understanding of complex biological systems.
  • FPSL offers a significant advancement over traditional histogram-based methods.