Intelligent Approach for Analyzing Semiconductor Band Gaps in Nanomaterial Systems
| Authors | Bhagyashree Ashok Tingare1, R.A. Kapgate2, P. William3 , Jaikumar M. Patil4, Tarun Dhar Diwan5 , Prasad M. Patare6, Laxmikant S Dhamande6 |
| Affiliations |
1Department of Artificial Intelligence and Data Science, D Y Patil College of Engineering, Akurdi, Pune 2Department of Mechatronics Engineering, Sanjivani College of Engineering, Kopargaon, MH, India 3Department of Information Technology, Sanjivani College of Engineering, Kopargaon, MH, India 4Department of Computer Science and Engineering, Shri Sant Gajanan Maharaj College of Engineering, Shegaon, SGBAU, Amravati 5Controller of Examination (COE), Atal Bihari Vajpayee University, Bilaspur, India 6Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, MH, India |
| Е-mail | tarunctech@gmail.com |
| Issue | Volume 17, Year 2025, Number 4 |
| Dates | Received 10 April 2025; revised manuscript received 15 August 2025; published online 29 August 2025 |
| Citation | Bhagyashree Ashok Tingare, R.A. Kapgate, P. William, et al., J. Nano- Electron. Phys. 17 No 4, 04027 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(4).04027 |
| PACS Number(s) | 07.05.Mh, 73.20.At |
| Keywords | Machine learning, Semiconductor band gaps, Nanomaterials (4) , Fine-tuned White Shark algorithm-resilient XGBoost (FWS-RXGBoost). |
| Annotation |
Analysis of semiconductor band gaps in nanomaterials is of great importance for electronics applications. Traditional approaches have limitations in dealing with complex, nonlinear relationships for the prediction of band gaps. This study proposes a Fine-Tuned White Shark Algorithm-Resilient XGBoost (FWS-RXGBoost) model that eliminates the challenges associated with optimizing the hyperparameters of XGBoost for more robust predictions. A Kaggle dataset of material fingerprints and target band gap values are used. To ensure that the model accuracy, feature normalization by Z-score at preprocessing stage standardizes the features, which enhances the gradient-based learning. Optimization inspired by White Shark achieves a balance between the global exploration and local exploitation. This model is proven to be more resilient with noise in data. Comparisons have been made with a gradient boosting model and the Extra Trees model. According to RMSE (0.17), MAE (0.10), and R² score (0.97), FWS-RXGBoost is effective at modeling complex dependencies related to band gap predictions. In this regard, these results show that FWS-RXGBoost is a reliable, high-accuracy tool for the prediction of semiconductor band gaps and is presently ready for application in any real-world settings where accuracy is critical. Here, more varied datasets and sophisticated hybrid models may be used in future studies to increase prediction capabilities. |
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List of References |
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