Towards a Unified AI-Driven Quantum Framework: Beyond Density Functional Theory for 3D Materials

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.