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Thermodynamic Potentials

Thermodynamic Potentials

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...

Maxwell-Boltzmann Distribution: Problem Solving

Maxwell-Boltzmann Distribution: Problem Solving

Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by

Thermodynamic Systems

Thermodynamic Systems

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.
Consider an example of tea boiling in a kettle. The...

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.

First Law Of Thermodynamics: Problem-Solving

First Law Of Thermodynamics: Problem-Solving

The first law of thermodynamics states that the change in internal energy of the system is equal to the net heat transfer into the system minus the net work done by the system. This equation is a generalized form of energy conservation and can be applied to any thermodynamic process.
The following strategies can be used to solve any problem involving the first law of thermodynamics.

Maxwell's Thermodynamic Relations

Maxwell's Thermodynamic Relations

Maxwell's thermodynamic relations are very useful in solving problems in thermodynamics. Each of Maxwell's relations relates a partial differential between quantities that can be hard to measure experimentally to a partial differential between quantities that can be easily measured. These relations are a set of equations derivable from the symmetry of the second derivatives and the thermodynamic potentials.
All thermodynamic potentials are exact differentials. Therefore, their second-order...

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Multi-Task Feedforward Neural Networks for Thermodynamic Property Prediction under Small Sample Sizes.

Gezhao Sang^1,2, Zhengyi Xu^1,2, Jianming Wei^1,2

¹Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.

The Journal of Physical Chemistry. A

|January 16, 2026

Summary

This study introduces ThermoMTLnet, a novel machine learning model for predicting thermodynamic properties of hazardous chemicals. It excels in small-sample scenarios, improving accuracy and generalization for explosion modeling.

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Area of Science:

Computational Chemistry
Chemical Engineering
Materials Science

Background:

Accurate thermodynamic properties are crucial for reliable explosion modeling of hazardous chemicals.
Experimental data scarcity necessitates predictive approaches for these properties.
Existing machine learning quantitative structure-property relationship (ML-QSPR) methods face challenges in multitask accuracy, feature redundancy, and generalization with limited data.

Purpose of the Study:

To develop a novel machine learning model for accurate thermodynamic property prediction, particularly under small-sample conditions.
To address limitations of current ML-QSPR methods, including prediction accuracy, feature selection, and generalization.
To enhance the reliability of explosion modeling through improved thermodynamic property prediction.

Main Methods:

Proposed a thermodynamically constrained multitask learning network (ThermoMTLnet).
Leveraged multitask learning to capture correlations among thermodynamic properties.
Integrated ensemble learning for feature engineering to mitigate overfitting.
Incorporated physicochemical constraints via a physics-informed neural network (PINN) into the loss function.

Main Results:

ThermoMTLnet demonstrated superior performance compared to traditional ML models and single-task networks.
Achieved a 0.23% higher Pearson correlation coefficient (PCC) and 2.44% lower mean absolute error (MAE) on 300-sample datasets compared to the best baseline.
Maintained consistent superiority on larger datasets, indicating robust generalization capabilities.

Conclusions:

ThermoMTLnet effectively predicts thermodynamic properties, especially with limited data.
The model's approach enhances structure-property relationship modeling through multitask learning and physics-informed constraints.
This work provides a valuable tool for improving explosion modeling and chemical safety assessments.