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相关概念视频

Network Covalent Solids02:18

Network Covalent Solids

13.5K
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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Bonding in Metals02:32

Bonding in Metals

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Metallic bonds are formed between two metal atoms. A simplified model to describe metallic bonding has been developed by Paul Drüde called the “Electron Sea Model”. 
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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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Crystal Field Theory - Octahedral Complexes02:58

Crystal Field Theory - Octahedral Complexes

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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...
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Introduction to Chemical Bonds01:01

Introduction to Chemical Bonds

8.1K
Chemical Bonds
The electrons of the outermost energy level determine the energetic stability of the atom and its tendency to form chemical bonds with other atoms. The innermost electron shell has a maximum capacity of two electrons, but the next two electron shells can each have a maximum of eight electrons. This is known as the octet rule, which states that, with the exception of the innermost shell, atoms are most stable energetically when they have eight electrons in their valence shell, the...
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Covalent Bonding and Lewis Structures02:46

Covalent Bonding and Lewis Structures

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Compared to ionic bonds, which results from the transfer of electrons between metallic and nonmetallic atoms, covalent bonds result from the mutual attraction of atoms for a “shared” pair of electrons.
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Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations
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一个量子化学结合数据库,用于固态材料.

Aakash Ashok Naik1,2, Christina Ertural1, Nidal Dhamrait1

  • 1Federal Institute for Materials Research and Testing, Department Materials Chemistry, Berlin, 12205, Germany.

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|September 11, 2023
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概括
此摘要是机器生成的。

了解化学键是材料科学的关键. 这项研究使用VASP和LOBSTER软件分析1520个化合物的结合,创建了一个改进机器学习材料特性预测的数据库.

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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Quantitative Atomic-Site Analysis of Functional Dopants/Point Defects in Crystalline Materials by Electron-Channeling-Enhanced Microanalysis
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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学计算化学
  • 固态物理 固态物理

背景情况:

  • 了解化学键对于预测材料性质至关重要.
  • 密度函数理论 (DFT) 等计算方法为电子结构提供了洞察力.
  • 分析粘合信息可以增强数据驱动的材料发现.

研究的目的:

  • 为绝缘体和半导体创建一个全面的化学结合信息数据库.
  • 为了证明粘合描述符在机器学习模型中对材料属性的实用性.
  • 为了提高使用量子化学结合特征预测音声性质的准确性.

主要方法:

  • 使用VASP和LOBSTER软件包进行自动数据生成.
  • 对包括绝缘体和半导体在内的1520种化合物进行了粘合分析.
  • 预测的平面基于波的波函数在原子轨道基础上运行.

主要成果:

  • 创建了一个数据库,该数据库包含了预测的状态密度和结合指标.
  • 与标准的DFT计算和启发式计算对比的基准债券指标.
  • 开发了一种用于声学属性的机器学习模型,该模型显示精度增加了27%.

结论:

  • 量子化学键特性显著提高了机器学习模型的性能.
  • 生成的数据库为材料研究提供了宝贵的见解.
  • 用于粘合分析的自动化工作流加速材料发现.