Towards a Unified AI-Driven Quantum Framework: Beyond Density Functional Theory for 3D Materials - Barack Ndenga (Publisher, 2024)
Density Functional Theory (DFT) is the cornerstone of modern quantum simulations but suffers from functional approximations and high computational cost for complex 3D materials. I propose a unified AI-driven quantum framework combining DFT, artificial intelligence (AI) corrections, and machine-learned force fields (MLFFs). This hybrid approach corrects DFT errors using neural networks and generalizes results into efficient MLFFs for large-scale 3D simulations. The framework achieves near ab initio accuracy at reduced computational cost, enabling predictive simulations of crystalline solids, nanostructures, and energy materials. This work represents a paradigm shift in computational chemistry and materials science by unifying physics-based and data-driven models.
This work introduces a unified AI-driven quantum framework for the simulation of 3D materials, integrating Density Functional Theory (DFT), Artificial Intelligence (AI) corrections, and Machine-Learned Force Fields (MLFFs). The approach addresses the long-standing trade-off between accuracy and scalability in computational chemistry, offering a paradigm shift towards intelligent quantum simulations. The proposed framework demonstrates that physics-based theory and data-driven intelligence can coexist symbiotically, enabling simulations with quantum-level fidelity at computational costs suitable for large-scale and complex material systems. This article represents my 14th scientific publication, and it sets the foundation for a new generation of computational tools in materials science, nanotechnology, and condensed matter physics. "The future of quantum materials lies not in choosing between physics and data, but in unifying them into a single intelligent framework." — Ndenga Lumbu Barack Alias BarackEinstein97