Leveraging Artificial Intelligence to Predict Electronic Structures in Nanoparticles
| Authors | Ganesh Punjaba Dawange1 , P. William2 , Prasad M Patare3, Tarun Dhar Diwan4 , Yogeesh N5 , R.A. Kapgate6 , Sharmila7 |
| Affiliations |
1Engineering Science and Humanities, Sanjivani College of Engineering, Kopargaon, India 2Department of Information Technology, Sanjivani College of Engineering, Kopargaon, MH, India 3Department of Mechanical Engineering, Sanjivani College of Engineering, Kopargaon, MH, India 4Controller of Examination (COE), Atal Bihari Vajpayee University, Bilaspur, India 5Department of Mathematics, Government First Grade College, Tumkur, Karnataka, India 6Department of Mechatronics Engineering, Sanjivani College of Engineering, Kopargaon, MH, India 7Department of ECE, Raj Kumar Goel Institute of Technology, Ghaziabad, India |
| Е-mail | tarunctech@gmail.com |
| Issue | Volume 17, Year 2025, Number 5 |
| Dates | Received 10 August 2025; revised manuscript received 20 October 2025; published online 30 October 2025 |
| Citation | Ganesh Punjaba Dawange, P. William, Prasad M Patare, et al., J. Nano- Electron. Phys. 17 No 5, 05037 (2025) |
| DOI | https://doi.org/10.21272/jnep.17(5).05037 |
| PACS Number(s) | 07.05.Mh, 73.22. – f |
| Keywords | Nanoparticles (70) , Electronic structure prediction, Machine learning, Density functional theory (5) , Nanotechnology (6) . |
| Annotation |
The application of artificial intelligence (AI) techniques in predicting electronic structures of nanoparticles is a complex task traditionally reliant on quantum mechanical calculations. The unique properties of nanoparticles, driven by quantum confinement effects at the nanoscale, are crucial in fields such as catalysis, electronics, and medicine. The study utilize advanced computational models, specifically Adaptive Tunicate Swarm Optimized Graph Neural Networks (ATSO-GNN), to accurately predict electronic density, energy states, and other properties of nanoparticles. The approach comprises data preprocessing with z-score normalization and feature extraction utilizing Linear Discriminant Analysis (LDA), which improves model sensitivity to minor electrical fluctuations. The ATSO-GNN model, trained on structural data from a nanoparticle dataset, demonstrates significant improvements in accuracy and computational efficiency over traditional methods like Density Functional Theory (DFT). Results indicate that the approach effectively captures complex atomic interactions, making it valuable for materials science and nanotechnology applications where rapid and precise electronic structure predictions are essential. Compared to standard methods, the ATSO-GNN model offers higher R2 (0.95) lower mean absolute error (MAE) (0.2) and lower computation times (1.5) enhanced prediction. This study demonstrates how AI-based methods significantly improve the speed and accuracy of electronic structure predictions. |
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List of References |
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