High-Throughput Computational Screening of Two-Dimensional Semiconductors.

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Chemistry

Background:

• Two-dimensional (2D) materials possess unique properties crucial for next-generation nanoscale devices.
• Discovering new 2D semiconductors with specific electronic and stability characteristics is essential for technological advancement.

Purpose of the Study:

• To computationally screen a large number of 2D nonmagnetic semiconductors.
• To establish a comprehensive database (2DSdb) of stable and conductive 2D materials.
• To develop an efficient predictive model for key electronic properties.

Main Methods:

• High-throughput first-principles calculations.
• Semiempirical van der Waals dispersion correction.
• Screening based on thermodynamic, mechanical, dynamic, and thermal stability criteria.
• Development of a linear fitting model for property prediction.

Main Results:

• Identified 73 direct-gap and 183 indirect-gap 2D nonmagnetic semiconductors.
• Calculated and presented extensive material properties including lattice constants, formation energy, elastic moduli, effective mass, and electronic properties.
• Established the 2D semiconductor database (2DSdb) accessible online.
• Proposed a linear fitting model for predicting band gap, ionization energy, and electron affinity with high accuracy and low computational cost.

Conclusions:

• The 2DSdb serves as a valuable resource for designing novel 2D materials and heterostructures for applications in photocatalysis and nanoscale devices.
• The developed predictive model offers a computationally efficient alternative to hybrid density functional theory (DFT) for evaluating 2D semiconductor properties.