Advancing Electronic Structure Modeling for Next Generation Quantum Materials
DOI:
https://doi.org/10.54536/jsere.v1i2.6771Keywords:
Electron Correlation, Electronic Structure Modeling, Physics-Informed Machine Learning, Quantum Materials, Spin-Orbit Coupling, Virtual Material ScreeningAbstract
This study presents a reliable and efficient electronic structure modeling framework for next-generation quantum materials, which is directly applicable to practical applications. The proposed hybrid model uses Density Functional Theory, many-body correction, and physics-informed machine learning to accurately predict the band gap, quasiparticle energy, and density of states. The model’s average accuracy compared with experimental data was 0.89, significantly higher than 0.78 for conventional DFT and 0.84 for DFT plus GW. This precision simplifies the selection of suitable materials for quantum computing components, spintronic devices, and topological electronic systems. The study shows that electron correlation contributes about 34 percent, spin-orbit coupling contributes 26 percent, and lattice distortion contributes 21 percent, all of which directly affect device performance. It is possible to reduce the time and cost of experimental research by approximately 40-50% through virtual screening. As a result, this work can serve as a powerful decision-support tool for industrial researchers and policymakers, making a real and sustainable contribution to future quantum technology development.
References
Adesso, Gerardo, Rosario Lo Franco, and Valentina Parigi. 2018. “Foundations of Quantum Mechanics and Their Impact on Contemporary Society Subject Areas.”
Benaissa, Mohammed et al. 2025. “Simple Approach to More Efficient Density Functional Theory Simulations.” Computational Condensed Matter 42: e01012. https://www.sciencedirect.com/science/article/pii/S2352214325000115.
Biz, Chiara, Mauro Fianchini, and Jose Gracia. 2021. “Strongly Correlated Electrons in Catalysis : Focus on Quantum Exchange.”
Cangi, Attila et al. 2025. “Materials Learning Algorithms ( MALA ): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations.” Computer Physics Communications 314(November 2024): 109654. https://doi.org/10.1016/j.cpc.2025.109654.
Chattopadhyay, Tapas Kumar. 2025. “Spintronics and Quantum Computing Devices.” 7(5): 1–17.
Gao, Weiwei, Weiyi Xia, Xiang Gao, and Peihong Zhang. 2016. “Speeding up GW Calculations to Meet the Challenge of Large Scale Quasiparticle Predictions.” Nature Publishing Group: 1–8. http://dx.doi.org/10.1038/srep36849.
Hu, Haixia et al. 2025. “Revealing Electron Transport Connectivity as an Important Factor in Fl Uencing Stability of Organic Solar Cells.” : 1–14.
Hu, Yong et al. 2021. “Topological Surface States and Flat Bands in the Kagome Superconductor CsV3Sb5.” Science Bulletin 67.
Liu, Xiaoming et al. 2025. “From Black Box to Glass Box : A Practical Review of Explainable Artificial Intelligence ( XAI ).” 6: 1–19.
López-paz, Sara A, and Fabian O Von Rohr. 2022. “Quantum Materials Discovery by Combining Chemical and Physical Design Principles.” 76(7): 628–34.
Malet, Francesc. “Exchange-Correlation Functionals from the Strongly-Interacting Limit of DFT: Applications to Model Chemical Systems.”
Mariotti, Ettore, José María, Alonso Moral, and Albert Gatt. 2023. “Exploring the Balance between Interpretability and Performance with Carefully Designed Constrainable Neural Additive Models.” Information Fusion 99(June): 101882. https://doi.org/10.1016/j.inffus.2023.101882.
Meunier, Vincent. 2023. “Mean-Field Approximation of the Hubbard Model Expressed in a Many-Body Basis.”
Nair, Akhil S, Lucas Foppa, and Matthias Sche. 2025. “Materials Database from All-Electron Hybrid Functional DFT Calculations.” : 1–8.
Ramachandran, Anand. 2024. “Quantum Materials and Engineering Transformative Advances, Persistent Challenges, and Emerging Opportunities for Future Technologies.”
Rao, Tingke et al. 2022. “Low Dimensional Transition Metal Oxide towards Advanced Electrochromic Devices.” Nano Energy 100: 107479. https://www.sciencedirect.com/science/article/pii/S2211285522005572.
Sahni, Professor. 2004. “The Hohenberg-Kohn Theorems and Kohn-Sham Density Functional Theory.” In , 99–123.
Selisko, Johannes et al. 2025. “Dynamical Mean Fi Eld Theory for Real Materials on a Quantum Computer Check for Updates.” : 19–21.
Six, Cs et al. 2023. “Theoretical and Computational Exploration of Electronic Structure , Optical Properties , Open Circuit Voltage , and Toxicity of Perovskites Solar Cell :” Hybrid Advances 4(March): 100084. https://doi.org/10.1016/j.hybadv.2023.100084.
Vollhardt, Dieter. 2012. “Dynamical Mean-Field Theory for Correlated Electrons.” Annalen der Physik 524: 1–19.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Md. Monirul Islam
This work is licensed under a Creative Commons Attribution 4.0 International License.