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Related Concept Videos

Calculating Standard Free Energy Changes02:49

Calculating Standard Free Energy Changes

23.0K
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...
23.0K
Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

12.0K
The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
 
where R is the gas constant (8.314 J/K·mol), T is the absolute temperature in kelvin, and Q is the reaction quotient. This equation may be used to predict the spontaneity of a process under any given set of conditions.
Reaction Quotient...
12.0K
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

25.2K
An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
25.2K
Gibbs Free Energy02:39

Gibbs Free Energy

35.3K
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...
35.3K
Effects of Temperature on Free Energy02:11

Effects of Temperature on Free Energy

26.4K
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:
26.4K
Thermodynamic Potentials01:26

Thermodynamic Potentials

1.1K
Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
1.1K

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Related Experiment Video

Updated: Oct 19, 2025

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization
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Recent developments in multiscale free energy simulations.

Emilia P Barros1, Benjamin Ries1, Lennard Böselt1

  • 1Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093, Zurich, Switzerland.

Current Opinion in Structural Biology
|September 17, 2021
PubMed
Summary

Physics-based free energy simulations are advancing across scales. Machine learning integration enhances accuracy and feasibility for complex system modeling and property prediction.

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Area of Science:

  • Computational chemistry and physics
  • Molecular modeling and simulation
  • Machine learning in science

Background:

  • Physics-based free energy simulations rigorously calculate molecular properties like binding affinities.
  • Historically limited to atomistic scales, recent advances enable multiscale modeling.
  • Complex systems and high-accuracy predictions require overcoming scale limitations.

Purpose of the Study:

  • To review recent methodological advances in multiscale free energy simulations.
  • To explore the opportunities presented by machine learning in this field.
  • To highlight improvements in accuracy, feasibility, and scale-crossing capabilities.

Main Methods:

  • Discussion of physics-based free energy simulation techniques.
  • Integration of machine learning approaches into simulation workflows.
  • Analysis of methods that bridge temporal, spatial, and theoretical scales.

Main Results:

  • Methodological advances allow reliable crossing of temporal, spatial, and theory scales.
  • Machine learning improves accuracy and feasibility of simulations.
  • New opportunities arise for modeling complex systems and predicting properties.

Conclusions:

  • Multiscale free energy simulations are becoming more powerful and versatile.
  • Machine learning is a key enabler for pushing the boundaries of these simulations.
  • These advancements facilitate accurate predictions at reduced computational cost.