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High-throughput Identification and Characterization of Two-dimensional Materials using Density functional theory.

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A new criterion identifies 1356 potential two-dimensional (2D) materials by comparing experimental and calculated lattice constants. Most identified materials show favorable exfoliation energies, validating the approach for discovering novel 2D materials.

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Materials Science

Background:

  • Identifying novel two-dimensional (2D) materials is crucial for advancing nanotechnology and electronics.
  • Existing methods for predicting 2D materials can be computationally intensive or limited in scope.
  • The Materials Project (MP) offers a vast repository of calculated material properties.

Purpose of the Study:

  • To develop a simple and efficient criterion for predicting candidate two-dimensional (2D) materials.
  • To computationally screen a large number of materials for their potential as 2D materials.
  • To create a database of predicted 2D materials and their properties.

Main Methods:

  • A criterion based on a >= 5% relative difference between experimental and Materials Project (MP) density functional theory (DFT) lattice constants was proposed.
  • Computational screening identified at least 1356 candidate 2D materials.
  • Energetics, structural, electronic, and elastic properties were calculated for bulk and single-layer systems.

Main Results:

  • Over 1356 potential 2D materials were predicted using the lattice constant comparison criterion.
  • Calculated exfoliation energies confirmed that 88.9% of the predicted materials meet established criteria for exfoliation.
  • A database of 1012 bulk and 430 single-layer materials, with 371 common systems, was established.

Conclusions:

  • The proposed lattice constant comparison criterion is an effective method for identifying promising 2D material candidates.
  • Experimental validation using molybdenum telluride supports the reliability of the computational predictions.
  • The publicly available data facilitates further research into novel 2D materials.