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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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Published on: May 28, 2016

Universal machine learning framework for defect predictions in zinc blende semiconductors.

Arun Mannodi-Kanakkithodi^1,2, Xiaofeng Xiang³, Laura Jacoby⁴

¹Center for Nanoscale Materials, Argonne National Laboratory, Argonne, IL 60439, USA.

Patterns (New York, N.Y.)

|May 5, 2022

Summary

We developed a machine learning framework to predict and screen functional impurities in semiconductors, identifying promising candidates for improved material properties.

Keywords:

combinatorial screening computational materials science density functional theory high-throughput data machine learning materials informatics mid-gap states point defects semiconductors

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Area of Science:

Materials Science
Computational Chemistry
Solid State Physics

Background:

Semiconductor functional impurities significantly impact material properties.
Predicting impurity behavior requires extensive computational resources.

Purpose of the Study:

To develop a machine learning (ML) framework for predicting and screening functional impurities in semiconductors.
To accelerate the discovery of novel semiconductor materials with tailored properties.

Main Methods:

Utilized high-throughput density functional theory (DFT) computations to generate a dataset of defect formation energies.
Employed machine learning regression models trained on elemental and defect properties.
Screened a wide range of elements as impurities across various semiconductor types.

Main Results:

Developed ML models capable of predicting impurity formation energies and charge transition levels.
Successfully screened approximately 12,000 impurity-semiconductor combinations.
Identified impurities with lower formation energies than native defects in candidate semiconductors.

Conclusions:

The ML-DFT framework enables efficient prediction and screening of functional impurities.
This approach accelerates the discovery of advanced semiconductor materials.
The developed framework can guide experimental efforts in materials design.