Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms
The third-generation semiconductors have the characteristics of a large bandgap, a high breakdown electric field, a fast electron saturation rate, high-temperature resistance, corrosion resistance, and radiation resistance, making them the preferred core materials and devices for cutting-edge high-t...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Crystals |
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| Online Access: | https://www.mdpi.com/2073-4352/15/3/225 |
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| author | Hang Zhou Jie Wu Jiangtao Yang Qingyang Fan |
| author_facet | Hang Zhou Jie Wu Jiangtao Yang Qingyang Fan |
| author_sort | Hang Zhou |
| collection | DOAJ |
| description | The third-generation semiconductors have the characteristics of a large bandgap, a high breakdown electric field, a fast electron saturation rate, high-temperature resistance, corrosion resistance, and radiation resistance, making them the preferred core materials and devices for cutting-edge high-tech fields, such as mobile communication, new energy vehicles, and smart grids in the future. The III–V compound semiconductors are a typical representative of them. In order to discover and explore new III–V semiconductor materials more efficiently and accurately, this paper adopts a machine-learning method optimized by the beetle algorithm and combined with first-principle calculation verification to efficiently and accurately predict the performance of III–V nitride materials and study their physicochemical properties. This study improved the prediction efficiency of nitrogen-rich III–V semiconductor materials through the combination of machine learning and first principles, providing a new approach for the efficient and accurate prediction of semiconductor materials. |
| format | Article |
| id | doaj-art-4ac210e64fa24482b55f59ba63d9afa7 |
| institution | OA Journals |
| issn | 2073-4352 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Crystals |
| spelling | doaj-art-4ac210e64fa24482b55f59ba63d9afa72025-08-20T02:11:09ZengMDPI AGCrystals2073-43522025-02-0115322510.3390/cryst15030225Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning AlgorithmsHang Zhou0Jie Wu1Jiangtao Yang2Qingyang Fan3School of Computer Science and Technology, Kashi University, Kashi 844006, ChinaDepartment of Basic Courses Teaching and Reaching, Xi’an Traffic Engineering Institute, Xi’an 710300, ChinaCollege of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaCollege of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, ChinaThe third-generation semiconductors have the characteristics of a large bandgap, a high breakdown electric field, a fast electron saturation rate, high-temperature resistance, corrosion resistance, and radiation resistance, making them the preferred core materials and devices for cutting-edge high-tech fields, such as mobile communication, new energy vehicles, and smart grids in the future. The III–V compound semiconductors are a typical representative of them. In order to discover and explore new III–V semiconductor materials more efficiently and accurately, this paper adopts a machine-learning method optimized by the beetle algorithm and combined with first-principle calculation verification to efficiently and accurately predict the performance of III–V nitride materials and study their physicochemical properties. This study improved the prediction efficiency of nitrogen-rich III–V semiconductor materials through the combination of machine learning and first principles, providing a new approach for the efficient and accurate prediction of semiconductor materials.https://www.mdpi.com/2073-4352/15/3/225semiconductor materialperformance predictionfirst principlesmachine learning |
| spellingShingle | Hang Zhou Jie Wu Jiangtao Yang Qingyang Fan Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms Crystals semiconductor material performance prediction first principles machine learning |
| title | Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms |
| title_full | Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms |
| title_fullStr | Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms |
| title_full_unstemmed | Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms |
| title_short | Exploring New Nitrogen-Rich Compounds: Hybrid First-Principle Calculations and Machine-Learning Algorithms |
| title_sort | exploring new nitrogen rich compounds hybrid first principle calculations and machine learning algorithms |
| topic | semiconductor material performance prediction first principles machine learning |
| url | https://www.mdpi.com/2073-4352/15/3/225 |
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