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RESEARCH PAPER · Computational Materials Science · 2024

Machine learning regression model for predicting the band gap of multi-elements nonlinear optical crystals

Yaohui Yin, Wang Ai, Zhixin Sun, Chao Xin, Guangyong Jin

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Machine learning regression model for predicting the band gap of multi-elements nonlinear optical crystalsOriginalUser Behavior Analysis and Market Forecasting of Agricultural E-commerce Platforms2Predicting the Multiphotonic Absorption in Graphene by Machine Learning16Intelligent methods for assessing the efficiency of optical limiters based on carbon nanomaterials using the CORRELATO approach3Machine Learning Algorithms, Tools, and Databases for Applications in Materials Science0Exploring the structural basis of crystals that affect nonlinear optical responses: An experimental and machine learning quest12MELRSNet for accelerating the exploration of novel ultrawide bandgap semiconductors0Accurate and efficient prediction on the formation energy and potential profiles of sodium vanadium oxyfluorophosphate by machine learning3Artificial intelligence for post-moore field-effect transistors: a review and perspective1A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction12Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review16Advancements and Challenges in AlGaN and Phosphor-Based Deep Ultraviolet LEDs: Towards Broader Applications and Higher Efficiencies0High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification6High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification2A hybrid machine learning approach for high throughput screening of thermodynamically stable double perovskites with optimal band gap0Learning crystal–descriptor synergy: A multimodal deep learning approach for band gap prediction in chalcogenide materials0Machine Learning for Second‐ and Third‐Harmonic Generation: Benchmarks, Challenges, and a Roadmap for Data‐Driven Photonic Materials0Machine learning predictions of optical properties in metal-organic complexes: linear and nonlinear constants0

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  1. User Behavior Analysis and Market Forecasting of Agricultural E-commerce Platforms

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  2. Predicting the Multiphotonic Absorption in Graphene by Machine Learning

    AI · 2024-11-04

    José Zahid García-Córdova, Jose Alberto Arano-Martínez, C. Mercado-Zúñiga, Claudia Lizbeth Martínez‐González, C. Torres-Torres

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  3. Intelligent methods for assessing the efficiency of optical limiters based on carbon nanomaterials using the CORRELATO approach

    Physical Chemistry Chemical Physics · 2025-01-01

    Alexander Yu. Tolbin, Bogdan A. Tretyakov, Mikhail Sergeyevich Savelyev, Pavel N. Vasilevsky, Alexander Yu. Gerasimenko

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  4. Machine Learning Algorithms, Tools, and Databases for Applications in Materials Science

    Challenges and advances in computational chemistry and physics · 2025-01-01

    Nilima R. Das, Swayam Aryam Behera, P. Kali Krishna, Tripti Sharma, Patnala Ganga Raju Achary

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  5. Exploring the structural basis of crystals that affect nonlinear optical responses: An experimental and machine learning quest

    Optical Materials · 2025-02-06

    Abrar U. Hassan, Cihat Güleryüz, Islam H. El Azab, Ashraf Y. Elnaggar, M.H.H. Mahmoud

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  6. MELRSNet for accelerating the exploration of novel ultrawide bandgap semiconductors

    Microstructures · 2025-03-18

    Z.J. Zhang, Hongzhou Song, Yinghui Ji, Yan Cui, Xiang Li, Zili Zhang, Ziming Cai, Jie Zhang, Yunyi Wu, Huanxin Li, Bing Luo

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  7. Accurate and efficient prediction on the formation energy and potential profiles of sodium vanadium oxyfluorophosphate by machine learning

    Journal of Alloys and Compounds · 2025-06-25

    Lingzhi Wang, Xiaofei Sun, Jinbao Zhang, Haoxiang Li, Dan Yu, Jiayu Ju, Wenjun Wang, Xuesong Mei

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  8. Artificial intelligence for post-moore field-effect transistors: a review and perspective

    Materials Today Electronics · 2025-09-05

    Xiang Li, Nan Jiang, Wenhan Zhou, Xiaoyi Zhang, Yang Hu, Shuo Wang, Huipu Wang, Hengze Qu, Haibo Zeng, Shengli Zhang

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  9. A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction

    Crystals · 2025-10-28

    Mostafa Sadeghian, Arvydas Palevičius, Giedrius Janušas

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  10. Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review

    Materials Today Communications · 2025-12-20

    Juhi Jannat Mim, Abdullah Al Mamun, Mahtab Hossain Nayem, Suzon Mahmud, Antu Nath, Sayma Rahman, Shekh Asraful Fidal, Nayem Hossain

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  11. Advancements and Challenges in AlGaN and Phosphor-Based Deep Ultraviolet LEDs: Towards Broader Applications and Higher Efficiencies

    Awareness Journals · 2026-02-19

    K Vineela, P.V.N.M Kaushik, K. Jayanthi

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  12. High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification

    Angewandte Chemie International Edition · 2026-04-24

    Yan Xiao, Zhaoxi Yu, Yumiao Niu, Yidan Chen, Lin Shen, Daqing Yang, Ying Wang, Bingbing Zhang

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  13. High‐Performance Infrared Nonlinear Optical Crystals Discovery Guided by High‐Throughput Computation, Machine Learning, and Experimental Verification

    Angewandte Chemie · 2026-04-24

    Yan Xiao, Zhaoxi Yu, Yumiao Niu, Yidan Chen, Lin Shen, Daqing Yang, Ying Wang, Bingbing Zhang

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  14. A hybrid machine learning approach for high throughput screening of thermodynamically stable double perovskites with optimal band gap

    Materials Science and Engineering R Reports · 2026-06-23

    Zia Ur Rehman, Zijing Lin

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  15. Learning crystal–descriptor synergy: A multimodal deep learning approach for band gap prediction in chalcogenide materials

    Physica B Condensed Matter · 2026-07-14

    Hilal Kaya, Fatih Ekinci, Ahmet Yasin Selçuk, Neşet Koçkar, Özge DEMİR, Güney Ugurlu, Mehmet Serdar Güzel, Mehmet Serdar Güzel

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  16. Machine Learning for Second‐ and Third‐Harmonic Generation: Benchmarks, Challenges, and a Roadmap for Data‐Driven Photonic Materials

    Advanced Theory and Simulations · 2026-08-01

    Masoumeh Shokati Mojdehi, Ramin Khezri

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  17. Machine learning predictions of optical properties in metal-organic complexes: linear and nonlinear constants

    Molecular Crystals and Liquid Crystals · 2026-09-28

    Kalaivani H, Chitra Mangalaraj

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