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We developed a new AI method to quickly screen boron-nitrogen (B-N) codoped graphdiyne materials for electronic applications. This approach efficiently predicts material properties, aiding in the design of advanced optoelectronic devices and catalysts.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Artificial Intelligence in Materials

Background:

  • Doping is essential for tuning material electronic properties.
  • Traditional trial-and-error methods for material screening are inefficient due to vast chemical spaces.
  • Graphdiyne (GDY) is a promising material for various applications.

Purpose of the Study:

  • To develop a rapid screening method for boron-nitrogen (B-N) codoped graphdiyne (GDY).
  • To predict the band gap of B-N codoped GDY using artificial intelligence.
  • To establish a quantitative structure-property relationship for B-N doping in GDY.

Main Methods:

  • A connected convolutional neural network (CCNN) was employed for material screening.
  • A paired-atomic localized matrix (PALM) descriptor was designed to represent the local chemical environment.
  • Attribution analysis was performed to understand doping effects.

Main Results:

  • The CCNN model accurately predicted the band gap of B-N codoped GDY.
  • A quantitative relationship between material structure and band gap was established.
  • B-N doping at sp2 hybridized sites showed a greater impact on band gap broadening than at sp hybridized sites.

Conclusions:

  • The developed AI approach offers high accuracy and efficiency for materials screening.
  • This method facilitates the rational design of GDY-based materials for optoelectronic devices and catalysts.
  • It opens new possibilities for designing novel materials with desired electronic properties.