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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...

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.

Gibbs Free Energy and Thermodynamic Favorability

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:

Path Between Thermodynamics States

Path Between Thermodynamics States

Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data

Bowen Deng¹, Thijs Stuyver¹

¹Ecole Nationale Supérieure de Chimie de Paris, Université PSL, CNRS, Institute of Chemistry for Life and Health Sciences, 75 005 Paris, France.

Journal of Chemical Information and Modeling

|January 9, 2025

Summary

Graph-based deep learning models for predicting thermodynamic properties are sensitive to target definition and featurization. Molecule-level predictions show superior accuracy compared to atom-level increments.

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Area of Science:

Materials Science
Computational Chemistry
Machine Learning

Background:

Accurate prediction of thermodynamic properties is crucial for materials discovery and design.
Graph-based deep learning models offer a promising approach for predicting these properties.

Purpose of the Study:

To investigate the impact of target definition, data distribution, featurization, and model architectures on graph-based deep learning for thermodynamic property prediction.
To identify key factors influencing model accuracy and robustness.

Main Methods:

Evaluation of five diverse datasets with varying elemental composition, multiplicity, charge state, and size.
Analysis of different target definitions (formation vs. atomization energy/enthalpy).
Comparison of various featurization approaches and model architectures.

Main Results:

Target definition (formation energy) and featurization approach are critical for model accuracy.
Modest accuracy gains were observed through direct modification of model architectures.
Molecule-level predictions outperformed atom-level increment predictions, contrary to prior findings.

Conclusions:

Development of robust graph-based thermodynamic models requires careful consideration of target definition and featurization.
The findings suggest a path towards more universal graph-based models with enhanced accuracy across diverse datasets and compound domains.