How density functional theory and machine learning are integrated to accelerate the discovery, optimization, and inverse design of photocatalysts for solar hydrogen production. DFT descriptors of electronic structure and reaction energetics feed ML models (random forests, support-vector regression, neural networks) that predict band gaps, surface reactivity, and hydrogen-evolution rates, enabling high-throughput screening of binary photocatalytic systems and heterojunctions, with emerging quantum-machine-learning and generative approaches for exploring hypothetical materials.
A review of how density functional theory (DFT) and machine learning (ML) are combined to accelerate the discovery, optimization, and design of photocatalysts for solar hydrogen production by water s…