THEORETICAL FOUNDATIONS AND APPLICATION POSSIBILITIES OF MACHINE LEARNING ALGORITHMS IN SEMICONDUCTOR NANOSYSTEMS
1H.B. Ibrahimov, 2K.V. Tanriverdili
   download pdf   

ABSTRACT

This article provides a comprehensive analysis of the theoretical foundations and application areas of artificial neural networks (ANNs) for studying and modeling the complex physical and chemical properties of semiconductor nanostructures. Due to quantum size effects, surface phenomena, and the nonlinear nature of electron dynamics in semiconductor nanostructures, traditional modeling methods may be insufficiently effective. ANNs, on the other hand, find broad application in this field because of their capability to analyze complex data, detect patterns, and make predictions. The paper discusses in detail the technical principles of ANNs, learning methods, their application in semiconductor nanostructures, and future perspectives.

Keywords: Semiconductor, nanostructures, neural networks, machine learning
DOI:10.70784/azip.2.2025241

Received: 26.05.2025
Internet publishing: 30.05.2025    AJP Fizika A 2025 02 az p.41-43

AUTHORS & AFFILIATIONS

1. Institute of Physics Ministry of Science and Education Republic of Azerbaijan, 131 H.Javid ave. Baku, AZ 1073, Azerbaijan
2. Azerbaijan Technical University
E-mail: 2kenan.tanriverdili@gmail.com

 [1]   J. Xu, X. Wang, Y. Li (2021). Recent advances in semiconductor nanostructures for optoelectronic applications. Nano Today, 36, 101054. https://doi.org/10.1016/j.nantod.2020.101054
[2]   X. Liang, S. Zhang, G. Chen. Surface chemistry impact on semiconductor nanocrystals. Advanced Materials, 2022, 34(12), 2108903. https://doi.org/10.1002/adma.202108903
[3]   M. Schmitt, M. Heyl. Quantum simulations and computational complexity: Limits and opportunities. Nature Reviews Physics, 2022, 4, 689–700. https://doi.org/10.1038/s42254-022-00476-4
[4]   G. Carleo, M. Troyer. Solving the quantum many-body problem with artificial neural networks. Science, 2017, 355(6325), 602-606. https://doi.org/10.1126/science.aag2302
[5]   L. Wang, A. Zunger. Accurate modeling of semiconductor nanostructures: Challenges and perspectives. Journal of Applied Physics, 2020, 127(8), 080901. https://doi.org/10.1063/1.5143612
[6]   T. Xie, J.C. Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 2018, 120(14), 145301. https://doi.org/10.1103/PhysRevLett.120.145301
[7]   G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Zdeborova. Machine learning and the physical sciences. Reviews of Modern Physics, 2019, 91(4), 045002. https://doi.org/10.1103/RevModPhys.91.045002
[8]   K.T. Butler, D.W. Davies, H. Cartwright, O. Isayev, A. Walsh. Machine learning for molecular and materials science. Nature, 2018, 559(7715), 547–555. https://doi.org/10.1038/s41586-018-0337-2
[9]   T. Xie, J.C. Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 2018, 120(14), 145301. https://doi.org/10.1103/PhysRevLett.120.145301
[10]  Y.Yu. Tarasevich, A.V. Eserkepov, I.V. Vodolazskaya. Physics-informed neural networks for analyzing size effect and identifying parameters in piezoelectric semiconductor nanowires. Journal of Applied Physics, 2022, 137(2), 024303. https://doi.org/10.1063/5.0248278
[11]  R. Kumar, et al. Tuning optoelectronic properties of semiconductors with first principles modeling and machine learning. Materials Research Society Fall Meeting, 2021. https://doi.org/10.1557/adv.2021.324
[12]  T. Xie, J.C. Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Physical Review Letters, 2018, 120(14), 145301. https://doi.org/10.1103/PhysRevLett.120.145301