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Imperfections in Crystal Structure: Point, Line and Plane Defects01:25

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A perfect crystal, in theory, has a uniform structure with the same unit cell and lattice points throughout. However, any deviation from this periodic arrangement is known as an imperfection or defect. These defects can be categorized into three types: point, line, and plane defects.Point defects occur when there is a deviation from the ideal due to missing atoms, displaced atoms, or additional atoms. These imperfections might occur due to imperfect packing during crystallization or because of...
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Schottky defects arise when some lattice points in a crystal, such as those in NaCl, remain unoccupied, creating lattice vacancies without disturbing the overall electrical neutrality of the crystal. This defect is common in ionic crystals where the positive and negative ions are similar in size, as seen in sodium chloride and cesium chloride. The presence of Schottky defects enables the crystal to conduct electricity to a small extent through an ionic mechanism. Electric fields cause nearby...
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Non-stoichiometric defects refer to a type of defect in the crystal structure of a compound where the ratio of its constituent elements deviates from the ideal stoichiometric ratio. There are two main types of non-stoichiometric defects: metal excess defects and metal deficiency defects.Metal excess defects occur when there is a slight surplus of metal ions than what is required by the stoichiometric ratio of the compound. For example, heating a sodium chloride crystal in sodium vapor results...
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DeFecT-FF: a machine learning force field framework for high throughput defect modeling in CdTe-based solar cells.

Md Habibur Rahman1, Maitreyo Biswas1, Arun Mannodi-Kanakkithodi1

  • 1School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA. amannodi@purdue.edu.

Physical Chemistry Chemical Physics : PCCP
|April 16, 2026
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Summary

We created DeFecT-FF, a framework using machine learning force fields and DFT to predict defect properties in CdTe solar cells. This tool accelerates the discovery of new low-energy defect configurations for improved solar cell performance.

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Area of Science:

  • Materials Science
  • Computational Physics
  • Solid State Chemistry

Background:

  • CdTe-based solar cells are crucial for renewable energy.
  • Tailoring electronic and defect properties of CdTe absorber layers is key to improving solar cell efficiency.
  • Predicting defect behavior in complex alloys is computationally challenging.

Purpose of the Study:

  • To develop a computational framework for predicting defect properties in Cd/Zn-Te/Se/S compounds.
  • To accelerate the discovery of low-energy defect configurations.
  • To provide an accessible tool for researchers studying CdTe solar cells.

Main Methods:

  • High-throughput density functional theory (DFT) computations.
  • Crystal graph-based machine learning force field (MLFF) models trained on DFT data.
  • Active learning for dataset expansion and model refinement.

Main Results:

  • A comprehensive dataset of structures and energies for bulk and alloyed supercells with defects.
  • Accurate MLFF models capable of predicting energies and atomic forces across various charge states.
  • Identification of numerous new low-energy defect configurations and high-fidelity defect formation energy diagrams.

Conclusions:

  • The DeFecT-FF framework significantly accelerates defect prediction in CdTe-based materials.
  • The publicly available tool on nanoHUB enables researchers to bypass expensive DFT calculations.
  • This work facilitates the optimization of CdTe solar cells through advanced defect engineering.