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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.
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...
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Ionic Crystal Structures02:42

Ionic Crystal Structures

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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.
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...
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Network Covalent Solids02:18

Network Covalent Solids

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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...
14.5K
Trends in Lattice Energy: Ion Size and Charge02:54

Trends in Lattice Energy: Ion Size and Charge

24.2K
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:
24.2K
Structures of Solids02:22

Structures of Solids

14.6K
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...
14.6K
Metallic Solids02:37

Metallic Solids

18.7K
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.
All metallic solids exhibit high thermal and electrical conductivity, metallic luster, and malleability....
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Updated: Sep 10, 2025

Solid-state Graft Copolymer Electrolytes for Lithium Battery Applications
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完全固体電池のための固体電解質としてのカチオン乱高エントロピーガーネット構造:機械学習駆動の発見

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
まとめ
この要約は機械生成です。

この研究は,全固体電池のための高エントロピー固体電解質を迅速にスクリーニングするための機械学習フレームワークを導入します. この方法は,より安全で高エネルギー電池のための高いイオン伝導性を有する有望なガーネット型の材料を特定します.

キーワード:
高エントロピーのガーネット構造機械学習機械学習の原子間潜在力分子動力学固体電解質

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Elemental-sensitive Detection of the Chemistry in Batteries through Soft X-ray Absorption Spectroscopy and Resonant Inelastic X-ray Scattering
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Screening of Coatings for an All-Solid-State Battery Using In Situ Transmission Electron Microscopy
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科学分野:

  • 材料科学
  • 電気化学
  • コンピュータ化学

背景:

  • 高エントロピー固体電解質 (HE SSEs) は,完全固体電池 (ASSB) の性能と安全性を向上させます.
  • HE SSEの広大な化学空間と複雑さは,従来の実験的および計算的スクリーニング方法に挑戦しています.
  • 新しい HE SSE の発見を加速することは,バッテリー技術の進歩に不可欠です.

研究 の 目的:

  • カチオン乱高エントロピー (CDHE) グラネット型SSE候補の効率的なスクリーニングのための新しい機械学習 (ML) ベースの方法論を開発し,適用する.
  • 計算コストの削減と効率の向上による有望なHE SSEの探索を加速する.
  • 高いイオン伝導性を有する新しいCDHEガーネット型の材料をASSBで特定する.

主な方法:

  • 電子伝導性と熱力学的な安定性に基づいた4348のCDHEガーネット型SSE候補をスクリーニングするためにMLベースの代理モデルを使用した.
  • 安定した原子構成を決定し,デンドライト抑制のための弾性特性を計算するために,クリスタルハミルトングラフニューラルネットワーク (CHGNet) を採用した.
  • リチウムの拡散とイオン伝導性を評価するために,精密調整されたCHGNetポテンシャルで分子動力学 (MD) シミュレーションを行った.

主要な成果:

  • 電子伝導性と熱力学的好意性をフィルタリングしたCDHEガーネット型SSE候補の大量のデータセットをスクリーニングしました.
  • デンドライト形成を抑制し,インターフェイスの安定性を確保する可能性を示す,有利な弾性を持つ候補材料を特定した.
  • 室温で10−4S/cm以上のイオン伝導性を示す3つの有望なCDHEガーネット型SSE候補がMDシミュレーションで確認されました.

結論:

  • 開発されたMLベースのスクリーニングフレームワークは,高性能HE SSEの発見を大幅に加速します.
  • 特定されたCDHEの石榴石型の材料は,次世代の全固体電池の可能性を示しています.
  • このアプローチは,エネルギー貯蔵アプリケーションのための複雑な材料システムを探索するための計算効率の良い経路を提供します.