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Related Concept Videos

Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

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An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:
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IR Spectroscopy: Hooke's Law Approximation of Molecular Vibration01:16

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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
According to Hooke's law, the vibrational frequency is directly proportional to...
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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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Probing Lattice Anharmonicity and Thermal Transport in Ultralow-κ Materials Using Machine Learning Interatomic

Soham Mandal1, Ashutosh Srivastava2, Tanmoy Das1

  • 1Centre for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore, 560012, India.

Small (Weinheim an Der Bergstrasse, Germany)
|December 17, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning potentials reveal ultralow thermal conductivity in materials like TlAgSe and Cs2PbI2Cl2. This approach accurately models heat transport in strongly anharmonic solids, crucial for thermoelectrics and thermal barriers.

Keywords:
density functional theorymachine‐learning potentialmolecular‐dynamics simulationthermal conductivitythermoelectric

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Chemistry

Background:

  • Ultralow lattice thermal conductivity (κ) is critical for thermoelectric energy conversion and thermal barrier coatings.
  • Conventional theoretical models struggle with heat transport in strongly anharmonic materials where perturbation theory fails.
  • Understanding these materials requires advanced computational methods beyond traditional frameworks.

Purpose of the Study:

  • To develop and apply machine learning interatomic potentials (MLIP) for investigating thermal transport in anharmonic materials.
  • To explore heat transport mechanisms in TlAgSe and Cs2PbI2Cl2, focusing on their ultralow κ properties.
  • To provide a robust theoretical framework for designing materials with tailored thermal conductivity.

Main Methods:

  • Development of machine learning interatomic potentials (MLIP) for accurate interatomic force calculations.
  • MLIP-driven molecular dynamics (MD) simulations to analyze anharmonic lattice dynamics and structural properties.
  • Application of the Green-Kubo (GK) framework via equilibrium MD to compute lattice thermal conductivity (κ).

Main Results:

  • MLIP successfully modeled anharmonic lattice dynamics and finite-temperature distortions in TlAgSe and Cs2PbI2Cl2.
  • Calculated κ values using the non-perturbative GK framework closely matched experimental data.
  • Evidence of phonon scattering beyond the Ioffe-Regel limit and high anharmonicity (σA > 0.5) confirmed the materials' nature.

Conclusions:

  • The MLIP-integrated approach provides a powerful, non-perturbative method for studying heat transport in strongly anharmonic materials.
  • This framework enhances the physical understanding of thermal conductivity in materials like TlAgSe and Cs2PbI2Cl2.
  • The study offers guidance for the rational design of novel materials with ultralow thermal conductivity for advanced applications.