Defect modeling in semiconductors: the role of first principles simulations and machine learning
Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | JPhys Materials |
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| Online Access: | https://doi.org/10.1088/2515-7639/adb181 |
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| author | Md Habibur Rahman Arun Mannodi-Kanakkithodi |
| author_facet | Md Habibur Rahman Arun Mannodi-Kanakkithodi |
| author_sort | Md Habibur Rahman |
| collection | DOAJ |
| description | Point defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability. |
| format | Article |
| id | doaj-art-587bb3032c344752aa320edcd2275994 |
| institution | Kabale University |
| issn | 2515-7639 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | JPhys Materials |
| spelling | doaj-art-587bb3032c344752aa320edcd22759942025-02-12T09:13:43ZengIOP PublishingJPhys Materials2515-76392025-01-018202200110.1088/2515-7639/adb181Defect modeling in semiconductors: the role of first principles simulations and machine learningMd Habibur Rahman0https://orcid.org/0000-0002-7705-984XArun Mannodi-Kanakkithodi1https://orcid.org/0000-0003-0780-1583School of Materials Engineering, Purdue University , West Lafayette, IN 47907, United States of AmericaSchool of Materials Engineering, Purdue University , West Lafayette, IN 47907, United States of AmericaPoint defects in semiconductors dictate their electronic and optical properties. Vacancies, interstitials, substitutional defects, and defect complexes can form in the semiconductor lattice and significantly impact its performance in applications such as solar absorption, light emission, electronics, and catalysis. Understanding the nature and energetics of point defects is essential for the design and optimization of next-generation semiconductor technologies. Here, we provide a comprehensive overview of the current state of research on point defects in semiconductors, focusing on the application of density functional theory (DFT) and machine learning (ML) in accelerating the prediction and understanding of defect properties. DFT has been instrumental in accurately calculating defect formation energies, charge transition levels, and other defect-related properties such as carrier recombination rates and lifetimes, and ion migration barriers. ML techniques, particularly neural networks, have emerged as powerful tools for enabling rapid prediction of defect properties at DFT-accuracy in order to overcome the expense of using large supercells and advanced functionals. We begin this article with a discussion of different types of point defects and complexes, their impact on semiconductor properties, and the experimental and DFT approaches typically used for their characterization. Through multiple case studies, we explore how DFT has been successfully applied to understand defect behavior across a variety of semiconductors, and how ML approaches integrated with DFT can efficiently predict defect properties and facilitate the discovery of new materials with tailored defect behavior. Overall, the advent of ‘DFT+ML’ promises to drive advancements in semiconductor technology, catalysis, and renewable energy applications, paving the way for the development of high-performance semiconductors which are defect-tolerant or have desirable dopability.https://doi.org/10.1088/2515-7639/adb181defectssemiconductorsDFTmachine learning |
| spellingShingle | Md Habibur Rahman Arun Mannodi-Kanakkithodi Defect modeling in semiconductors: the role of first principles simulations and machine learning JPhys Materials defects semiconductors DFT machine learning |
| title | Defect modeling in semiconductors: the role of first principles simulations and machine learning |
| title_full | Defect modeling in semiconductors: the role of first principles simulations and machine learning |
| title_fullStr | Defect modeling in semiconductors: the role of first principles simulations and machine learning |
| title_full_unstemmed | Defect modeling in semiconductors: the role of first principles simulations and machine learning |
| title_short | Defect modeling in semiconductors: the role of first principles simulations and machine learning |
| title_sort | defect modeling in semiconductors the role of first principles simulations and machine learning |
| topic | defects semiconductors DFT machine learning |
| url | https://doi.org/10.1088/2515-7639/adb181 |
| work_keys_str_mv | AT mdhabiburrahman defectmodelinginsemiconductorstheroleoffirstprinciplessimulationsandmachinelearning AT arunmannodikanakkithodi defectmodelinginsemiconductorstheroleoffirstprinciplessimulationsandmachinelearning |