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Related Concept Videos

Chemical Bonds02:40

Chemical Bonds

16.3K

Atoms participate in a chemical bond formation to acquire a completed valence-shell electron configuration similar to that of the noble gas nearest to it in atomic number. Ionic, covalent, and metallic bonds are some of the important types of chemical bonds. Bond energy and bond length determine the strength of a chemical bond.
Types of Chemical Bonds
An ionic bond is formed due to electrostatic attraction between cations and anions. Often, the ions are formed by the transfer of electrons...
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MO Theory and Covalent Bonding02:40

MO Theory and Covalent Bonding

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The molecular orbital theory describes the distribution of electrons in molecules in a manner similar to the distribution of electrons in atomic orbitals. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Mathematically, the linear combination of atomic orbitals (LCAO) generates molecular orbitals. Combinations of in-phase atomic orbital wave functions result in regions with a high probability of electron density, while...
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Valence Bond Theory02:45

Valence Bond Theory

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Overview of Valence Bond Theory
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Molecular Orbital Theory II03:51

Molecular Orbital Theory II

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Molecular Orbital Energy Diagrams
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Types of Chemical Bonds02:37

Types of Chemical Bonds

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Chemical bonding theories were pioneered by American chemist Gilbert N. Lewis. He developed a model called the Lewis model to explain the type and formation of different bonds. Chemical bonding is central to chemistry; it explains how atoms or ions bond together to form molecules. It explains why some bonds are strong and others are weak, or why one carbon bonds with two oxygens and not three; why water is H2O and not H4O. 
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Bond Energies and Bond Lengths02:49

Bond Energies and Bond Lengths

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Stable molecules exist because covalent bonds hold the atoms together. The strength of a covalent bond is measured by the energy required to break it, that is, the energy necessary to separate the bonded atoms. Separating any pair of bonded atoms requires energy — the stronger a bond, the greater the energy required to break it.
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Related Experiment Video

Updated: Jun 13, 2025

From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding
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From Molecules to Materials: Engineering New Ionic Liquid Crystals Through Halogen Bonding

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Pursuing Extreme Descriptive Power and Generality in Chemical Bond Theories: A Method to Decipher "Interatomic

Xinxu Zhang1, Jiahao Wei1, Hui Jia1

  • 1Department of Physics and Tianjin Key Laboratory of Low Dimensional Materials Physics and Preparing Technology, School of Sciences, Tianjin University, Tianjin 300350, China.

Journal of Chemical Theory and Computation
|September 16, 2024
PubMed
Summary

A new machine learning framework encodes chemical bonds into detailed "genomes," overcoming limitations in traditional analysis. This approach offers unprecedented generality and descriptive power for understanding complex molecular and material properties.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Machine Learning

Background:

  • Traditional chemical bond analysis struggles with the vast information from electronic structure calculations, facing a 'Trilemma' of briefness, generality, and descriptiveness.
  • Existing methods often fail to simultaneously achieve high generality and descriptive power when analyzing complex chemical bonds.

Purpose of the Study:

  • To introduce a general machine learning (ML)-based framework for compacting chemical bond information into detailed residue-by-residue 'genomes'.
  • To overcome the limitations of traditional methods by achieving extreme generality and descriptive power in chemical bond analysis.
  • To demonstrate the framework's capability in analyzing critical S-Au and Au-Au bonds in gold nanoclusters.

Main Methods:

  • Developed a general ML framework fusing quantum mechanics, automated feature extraction, simulations (e.g., density functional theory), and generative models.
  • Encoded chemical bond information into 8-valued 'genomes' representing Bosonic-Fermionic character.
  • Trained the ML model on 26,528 electron localization function images from DFT simulations.

Main Results:

  • The ML framework generates information-dense, decodable genomes with 100% generality and broad descriptiveness, surpassing existing models.
  • Analysis of S-Au and Au-Au bonds in gold nanoclusters revealed details like bond polarization, hybridization, and atomic interactions.
  • Demonstrated integration of molecules and solids into genomes, enabling visualization of bond complexity and association with realistic indices.

Conclusions:

  • The proposed ML framework provides a powerful, general, and descriptive method for analyzing chemical bonds, particularly in complex systems like nanoclusters.
  • The 'genome' representation offers a novel way to 'understand' chemical bonds, with potential applications in chemisorption, molecular dynamics, and ultrafast processes.
  • This approach signifies a leap forward in computational chemistry, enabling deeper insights into interatomic interactions and material properties.