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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...
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A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
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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:
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Related Experiment Video

Updated: Jun 10, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Thermodynamics-consistent graph neural networks.

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|October 21, 2024
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Summary
This summary is machine-generated.

We developed graph neural networks (GE-GNNs) to accurately predict mixture properties. This method ensures thermodynamic consistency for activity coefficients, crucial for chemical process modeling.

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Area of Science:

  • Thermodynamics
  • Machine Learning
  • Chemical Engineering

Background:

  • Predicting activity coefficients is essential for chemical process design.
  • Existing models often face limitations in thermodynamic consistency and applicability.
  • Accurate prediction of composition-dependent properties is a key challenge.

Purpose of the Study:

  • To introduce a novel graph neural network (GE-NN) approach for predicting activity coefficients.
  • To ensure thermodynamic consistency in predictions using fundamental thermodynamic principles.
  • To develop a model free from thermodynamic modeling limitations.

Main Methods:

  • Developed excess Gibbs free energy graph neural networks (GE-GNNs).
  • Utilized automatic differentiation for end-to-end learning of activity coefficients.
  • Ensured thermodynamic consistency by predicting molar excess Gibbs free energy.

Main Results:

  • Achieved high accuracy in predicting activity coefficients for binary mixtures.
  • Demonstrated inherent thermodynamic consistency without additional loss terms.
  • The GE-GNN model showed no thermodynamic modeling limitations.

Conclusions:

  • GE-GNNs offer a powerful and thermodynamically consistent method for predicting mixture properties.
  • This approach advances the application of machine learning in chemical thermodynamics.
  • The model provides reliable predictions essential for chemical process optimization.