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Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

23.8K
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:
23.8K
Ionic Crystal Structures02:42

Ionic Crystal Structures

14.3K
Ionic crystals consist of two or more different kinds of ions that usually have different sizes. The packing of these ions into a crystal structure is more complex than the packing of metal atoms that are the same size.
Most monatomic ions behave as charged spheres, and their attraction for ions of opposite charge is the same in every direction. Consequently, stable structures for ionic compounds result (1) when ions of one charge are surrounded by as many ions as possible of the opposite...
14.3K
Molecular and Ionic Solids02:54

Molecular and Ionic Solids

17.1K
Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...
17.1K
Ionic Bonding and Electron Transfer02:48

Ionic Bonding and Electron Transfer

41.4K
Ions are atoms or molecules bearing an electrical charge. A cation (a positive ion) forms when a neutral atom loses one or more electrons from its valence shell, and an anion (a negative ion) forms when a neutral atom gains one or more electrons in its valence shell. Compounds composed of ions are called ionic compounds (or salts), and their constituent ions are held together by ionic bonds: electrostatic forces of attraction between oppositely charged cations and anions. 
41.4K
Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

26.4K
Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...
26.4K
The Born-Haber Cycle02:44

The Born-Haber Cycle

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Lattice Energy 
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Related Experiment Video

Updated: Jun 25, 2025

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
10:03

Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques

Published on: November 11, 2013

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Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts.

Vijay Choyal1, Nidhish Sagar1, Gopalakrishnan Sai Gautam1

  • 1Department of Materials Engineering, Indian Institute of Science, Bengaluru 560012, Karnataka, India.

Journal of Chemical Theory and Computation
|May 24, 2024
PubMed
Summary

Machine-learned interatomic potentials (MLIPs) accurately model complex battery materials. Artificial neural network potentials (AENET) show superior accuracy and transferability for disordered rocksalts, enabling new electrode discovery.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Energy Storage

Background:

  • Lithium-based disordered rocksalts (LDRs) are crucial for high-energy-density Li-ion batteries.
  • Conventional density functional theory (DFT) struggles with the complexity of LDRs.
  • Atom-centered machine-learned interatomic potentials (MLIPs) offer a promising alternative for modeling disordered systems.

Purpose of the Study:

  • To comprehensively evaluate the accuracy, transferability, and training efficiency of five atom-centered MLIPs for LDRs.
  • To assess MLIP performance in modeling an 11-component LDR chemical space.
  • To provide a benchmark for MLIPs in complex materials modeling.

Main Methods:

  • Generated a DFT-calculated dataset of 10,842 configurations for disordered LiTMO2 and TMO2 compositions (TM = transition metals).
  • Trained and evaluated Artificial Energy Network potentials (AENET), Gaussian Approximation Potentials (GAP), Spectral Neighbor Analysis Potentials (SNAP/qSNAP), and Moment Tensor Potentials (MTP).
  • Compared MLIP performance against Neural Equivariant Interatomic Potentials (NequIP) trained on a subset of the data.

Main Results:

  • AENET demonstrated the highest accuracy and transferability for energy predictions; MTP excelled in predicting atomic forces.
  • AENET showed fast initial training but significant time investment for error reduction (∼60% reduction at 3300 epochs).
  • AENET provided reasonable predictions (∼10% error) for Li-intercalation voltages in layered LiTMO2 frameworks compared to DFT.

Conclusions:

  • Atom-centered MLIPs, particularly AENET, are effective for modeling complex LDR materials.
  • The findings facilitate the discovery of novel disordered rockalt electrodes for advanced batteries.
  • This approach is applicable to other complex materials like high-entropy ceramics and alloys.