AI-driven materials discovery is transforming the way we explore and engineer novel compounds. By combining high-fidelity quantum simulations—such as density functional theory (DFT) and ab initio molecular dynamics—with machine‑learning surrogates, researchers can predict critical properties (band gaps, formation energies, mechanical strengths) in a fraction of the time. Neural networks and kernel-based models learn from a curated database of simulated or experimental results, enabling rapid interpolation across vast compositional and structural spaces. This synergy not only accelerates the identification of promising candidates but also reduces computational expense by delegating the bulk of costly quantum calculations to robust AI predictors.