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Network Covalent Solids02:18

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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.
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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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A Quantum-Chemical Bonding Database for Solid-State Materials.

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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Summary
This summary is machine-generated.

Understanding chemical bonds is key for materials science. This study uses VASP and LOBSTER software to analyze bonding in 1520 compounds, creating a database that improves machine learning predictions for material properties.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Solid-State Physics

Background:

  • Understanding chemical bonds is crucial for predicting material properties.
  • Computational methods like density functional theory (DFT) provide insights into electronic structure.
  • Analyzing bonding information can enhance data-driven material discovery.

Purpose of the Study:

  • To create a comprehensive database of chemical bonding information for insulators and semiconductors.
  • To demonstrate the utility of bonding descriptors in machine learning models for material properties.
  • To improve the accuracy of predicting phononic properties using quantum-chemical bonding features.

Main Methods:

  • Utilized VASP and LOBSTER software packages for automated data generation.
  • Performed bonding analysis on 1520 compounds, including insulators and semiconductors.
  • Projected plane wave-based wave functions onto an atomic orbital basis.

Main Results:

  • Generated a database of projected densities of states and bonding indicators.
  • Benchmarked bonding indicators against standard DFT computations and heuristics.
  • Developed a machine learning model for phononic properties that showed a 27% increase in accuracy.

Conclusions:

  • Quantum-chemical bonding features significantly enhance machine learning model performance.
  • The generated database provides valuable insights for materials research.
  • Automated workflows for bonding analysis accelerate materials discovery.