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Thermodynamics: Activity Coefficient

Thermodynamics: Activity Coefficient

Activity is the measure of the effective concentration of the species in solution. It can be expressed as the product of the molar concentration of the species and its activity coefficient. The activity coefficient is a dimensionless quantity and depends on the total ionic strength of the solution.
The activity coefficient is a measure of the deviation from ideal behavior. When the ionic strength of the solution is minimal, the activity coefficient of an ionic species is close to unity, making...

Factors Affecting Activity Coefficient

Factors Affecting Activity Coefficient

The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size.
The activity coefficient value for an ion is close to one when the solution has almost zero ionic strength, i.e., when the solution shows close to ideal behavior. As the ionic strength of the solution increases from 0 to 0.1 mol/L, a...

Arrhenius Plots

Arrhenius Plots

The Arrhenius equation relates the activation energy and the rate constant, k, for chemical reactions. In the Arrhenius equation, k = Ae−Ea/RT, R is the ideal gas constant, which has a value of 8.314 J/mol·K, T is the temperature on the kelvin scale, Ea is the activation energy in J/mole, e is the constant 2.7183, and A is a constant called the frequency factor, which is related to the frequency of collisions and the orientation of the reacting molecules.
The Arrhenius equation can be used...

Phase Diagrams

Phase Diagrams

A phase diagram combines plots of pressure versus temperature for the liquid-gas, solid-liquid, and solid-gas phase-transition equilibria of a substance. These diagrams indicate the physical states that exist under specific conditions of pressure and temperature and also provide the pressure dependence of the phase-transition temperatures (melting points, sublimation points, boiling points). Regions or areas labeled solid, liquid, and gas represent single phases, while lines or curves represent...

Phase Diagram

Phase Diagram

The phase of a given substance depends on the pressure and temperature. Thus, plots of pressure versus temperature showing the phase in each region provide considerable insights into the thermal properties of substances. Such plots are known as phase diagrams. For instance, in the phase diagram for water (Figure 1), the solid curve boundaries between the phases indicate phase transitions (i.e., temperatures and pressures at which the phases coexist).

Thermodynamics: Chemical Potential and Activity

Thermodynamics: Chemical Potential and Activity

The effective concentration of a species in a solution can be expressed precisely in terms of its activity. Activity considers the effect of electrolytes present in the vicinity of the species of interest and depends on the ionic strength of the solution. The activity of a species is expressed as the product of molar concentration and the activity coefficient of the species.
The thermodynamic equilibrium constant is more accurately defined in terms of activity rather than concentration.

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Accelerating Phase Diagram Construction Through Activity Coefficient Prediction.

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Accelerating Phase Diagram Construction Through Activity Coefficient Prediction.

Related Experiment Video

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

Phase Diagram Characterization Using Magnetic Beads as Liquid Carriers

Published on: September 4, 2015

Accelerating phase diagram construction through activity coefficient prediction.

Mohsen Farshad¹, Fathya Y M Salih¹, Dinis O Abranches²

¹Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA.

The Journal of Chemical Physics

|October 24, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a machine learning method to predict phase diagrams efficiently. By using Gaussian process models on Kirkwood-Buff Integrals, it reduces computational costs for complex mixtures.

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Area of Science:

Computational chemistry and thermodynamics.
Machine learning applications in physical sciences.

Background:

Predicting phase diagrams using molecular simulations is computationally intensive.
Accurate thermodynamic data is crucial for understanding mixture behavior.

Purpose of the Study:

To develop an efficient machine learning methodology for predicting phase behavior.
To reduce the computational cost associated with phase diagram determination.
To establish a predictive link between Kirkwood-Buff Integrals and activity coefficients.

Main Methods:

Training a Gaussian process (GP) model on Kirkwood-Buff Integrals (KBIs).
Utilizing KBIs to predict activity coefficients, which quantify deviations from ideality.
Applying the trained GP model to new Lennard-Jones mixtures without prior phase data.

Main Results:

Successfully predicted activity coefficients for new systems, bypassing direct coexistence simulations.
Demonstrated significant reduction in computational expense for phase behavior prediction.
Established a scalable framework for analyzing complex mixtures.

Conclusions:

The developed machine learning approach offers a computationally efficient alternative for phase diagram prediction.
This method is broadly applicable in computational thermodynamics for studying mixtures with tunable interactions.
Leveraging KBIs with GP models provides a powerful tool for accelerating thermodynamic calculations.