Towards a Unified AI-Driven Quantum Framework: Beyond Density Functional Theory for 3D Materials
Year: 2024
Type: preprint
Abstract: 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.
Source: AfricArXiv (OSF Preprints)
Author: Ndenga, Barack
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7082631842
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