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相关概念视频

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

21.8K
Lattice Energy 
21.8K

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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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构建和评估基于的无序岩盐的机器学习原子间潜力.

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
概括

机器学习的原子间潜能 (MLIP) 准确地模拟复杂的电池材料. 人工神经网络潜能 (AENET) 对无序的岩盐显示出卓越的准确性和可转移性,使新的电极发现成为可能.

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 储能 储能 储能 储能 储能 储能

背景情况:

  • 基于的无序岩盐 (LDR) 对高能量密度的离子电池至关重要.
  • 传统的密度函数理论 (DFT) 与LDRs的复杂性作斗争.
  • 以原子为中心的机器学习原子间潜力 (MLIP) 为建模无序系统提供了一个有希望的替代方案.

研究的目的:

  • 综合评估五个以原子为中心的MLIP对LDR的准确性,可转移性和训练效率.
  • 为了评估MLIP在建模11个组成的LDR化学空间中的性能.
  • 在复杂材料建模中为MLIP提供一个基准.

主要方法:

  • 创建了一个DFT计算的数据集,包含10842个不顺序的LiTMO2和TMO2组合物的配置 (TM=过渡金属).
  • 训练并评估了人工能源网络潜力 (AENET),高斯近似潜力 (GAP),光谱邻近分析潜力 (SNAP/qSNAP) 和动量张量潜力 (MTP).
  • 将MLIP性能与在数据子集上训练的神经等价原子间潜能 (NequIP) 进行比较.

主要成果:

  • AENET在能源预测方面表现出最高的准确性和可转移性;MTP在预测原子力方面表现出色.
  • AENET显示了快速的初始培训,但对减少错误进行了大量的时间投资 (在3300个时代减少了60%).

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Characterization of Electrode Materials for Lithium Ion and Sodium Ion Batteries Using Synchrotron Radiation Techniques
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  • 与DFT相比,AENET提供了与DFT相比的层级LiTMO2框架中的Li-间歇电压的合理预测 (~10%的误差).

    • 结论:

    • 以原子为中心的MLIP,特别是AENET,对于模拟复杂的LDR材料是非常有效的.
    • 这些发现有助于发现用于先进电池的新型无序岩电极.
    • 这种方法适用于其他复杂材料,如高陶和合金.