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Related Concept Videos

Molecular and Ionic Solids02:54

Molecular and Ionic Solids

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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
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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...
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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.
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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Trends in Lattice Energy: Ion Size and Charge02:54

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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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Structures of Solids02:22

Structures of Solids

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Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
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Metallic Solids02:37

Metallic Solids

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Metallic solids such as crystals of copper, aluminum, and iron are formed by metal atoms. The structure of metallic crystals is often described as a uniform distribution of atomic nuclei within a “sea” of delocalized electrons. The atoms within such a metallic solid are held together by a unique force known as metallic bonding that gives rise to many useful and varied bulk properties.
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Solid-state Graft Copolymer Electrolytes for Lithium Battery Applications
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Cation-Disordered High-Entropy Garnet Structures as Solid-State Electrolytes for All-Solid-State Batteries: Machine

Jiwon Sun1,2, JunHo Song1, Juo Kim1,3

  • 1School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.

ACS Applied Materials & Interfaces
|August 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning framework to rapidly screen high-entropy solid-state electrolytes for all-solid-state batteries. The method identifies promising garnet-type materials with high ionic conductivity for safer, high-energy batteries.

Keywords:
high-entropy garnet structuresmachine learningmachine learning interatomic potentialmolecular dynamicssolid-state electrolytes

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • High-entropy solid-state electrolytes (HE SSEs) offer enhanced performance and safety for all-solid-state batteries (ASSBs).
  • The vast chemical space and complexity of HE SSEs challenge traditional experimental and computational screening methods.
  • Accelerating the discovery of novel HE SSEs is crucial for advancing battery technology.

Purpose of the Study:

  • To develop and apply a novel machine learning (ML)-based methodology for efficient screening of cation-disordered high-entropy (CDHE) garnet-type SSE candidates.
  • To accelerate the exploration of promising HE SSEs with reduced computational cost and improved efficiency.
  • To identify novel CDHE garnet-type materials with high ionic conductivity for ASSBs.

Main Methods:

  • Utilized an ML-based surrogate model to screen 4348 CDHE garnet-type SSE candidates based on electronic conductivity and thermodynamic stability.
  • Employed crystal Hamiltonian graph neural network (CHGNet) to determine stable atomic configurations and calculate elastic properties for dendrite suppression.
  • Performed molecular dynamics (MD) simulations with fine-tuned CHGNet potential to evaluate lithium diffusion and ionic conductivity.

Main Results:

  • Successfully screened a large dataset of CDHE garnet-type SSE candidates, filtering for electronic conductivity and thermodynamic favorability.
  • Identified candidate materials with favorable elastic properties, indicating potential for suppressing dendrite formation and ensuring interfacial stability.
  • Confirmed three promising CDHE garnet-type SSE candidates exhibiting ionic conductivities greater than 10⁻⁴ S/cm at room temperature via MD simulations.

Conclusions:

  • The developed ML-based screening framework significantly accelerates the discovery of high-performance HE SSEs.
  • The identified CDHE garnet-type materials demonstrate potential for next-generation all-solid-state batteries.
  • This approach offers a computationally efficient pathway to explore complex material systems for energy storage applications.