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Thermodynamic Potentials01:26

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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 pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
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In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
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The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the...
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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.
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Autonomous thermodynamically informed database generation for machine-learned interatomic potentials and application

Vincent G Fletcher1, Albert P Bartók1,2, Livia B Pártay3

  • 1Department of Physics, University of Warwick, Coventry, UK.

Npj Computational Materials
|January 22, 2026
PubMed
Summary
This summary is machine-generated.

We developed an automated framework using Nested Sampling (NS) and Density Functional Theory (DFT) to create robust Machine-Learned Interatomic Potential (MLIP) models for predicting material properties under extreme conditions.

Keywords:
ChemistryMaterials scienceMathematics and computingPhysics

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Area of Science:

  • Computational Materials Science
  • Physical Chemistry
  • Machine Learning

Background:

  • Accurate prediction of material phase properties under diverse conditions is crucial for scientific advancement.
  • Existing methods for constructing training databases for Machine-Learned Interatomic Potential (MLIP) models can be labor-intensive and may introduce bias.
  • There is a need for automated, knowledge-independent approaches to generate comprehensive datasets for MLIP development.

Purpose of the Study:

  • To introduce a novel, automated framework for constructing training databases for MLIP models.
  • To enable the accurate prediction of phase properties across wide pressure and temperature ranges.
  • To develop a generalizable and transferable MLIP model with reduced computational cost.

Main Methods:

  • Utilized Nested Sampling (NS) to explore configuration space and generate thermodynamically relevant configurations.
  • Employed ab initio Density Functional Theory (DFT) for evaluating generated configurations.
  • Applied the Atomic Cluster Expansion (ACE) architecture to fit the MLIP model to the generated database.
  • Demonstrated the framework's efficacy by applying it to magnesium (Mg).

Main Results:

  • Developed an MLIP model for magnesium capable of accurately describing behavior from 0-600 GPa and 0-8000 K.
  • Successfully calculated phonon spectra, elastic constants, and the pressure-temperature phase diagram for magnesium.
  • The framework demonstrated robustness, transferability, and generality in MLIP model generation.
  • Achieved reduced computational cost compared to traditional methods.

Conclusions:

  • The proposed automated framework effectively generates high-quality training databases for MLIPs.
  • This approach facilitates the creation of accurate and reliable models for materials under extreme conditions.
  • The method offers a bias-free, efficient, and scalable solution for MLIP development in materials science.