Advancing Computational Modeling in Quantum Materials: A DFT-Based Approach to Electronic Structure and Material Properties

Abstract

Computational modeling is now a cornerstone of quantum materials research, where the density functional theory (DFT) provides a key tool at the heart of predicting electronic structure and material properties. We evaluate DFT methods, with a focus on exchange-correlation functional; computational efficiency enhancements; and the incorporation of machine learning (ML). The study leverages first-principles calculations for the analysis of band structures, DOS, and functional-dependent electronic properties variations for quantum materials (2D materials, (Graphene, MoS₂, TMDs), superconductors, topological insulators. Additionally, time-dependent DFT (TDDFT) and orbital-free DFT (OF-DFT) increase accuracy for large-scale simulations, which are limited by computational resources in complex materials. In fact, these new ML-assisted DFT techniques greatly improve the computational speed and predictive accuracy, thereby minimizing the conflicting options between computational cost and precision. The study further presents critical perspectives on defect engineering in the context of semiconductors, asserting its importance in tuning electronic properties for emerging fields such as Nano electronics and quantum computing applications. Standard DFT functional come with accuracy limitations, but by combining them with AI-based surrogate models and many-body physics such as DFT+DMFT, GW, and Quantum Monte Carlo methods, there are powerful options on both fronts. Such innovations are unlocked by more efficient, scalable, and precise quantum material simulations, facilitating advances in next-generation devices based on optoelectronics, spintronic, and superconductivity. This work highlights the potential of AI-assisted computational modeling to revolutionize quantum materials science and ultimately enable advances in energy-efficient electronics and quantum technologies.