Quantum-enhanced machine learning framework for photocatalytic materials discovery: integrating quantum chemical descriptors and many-body electronic structure theory

Photocatalytic materials form an important class of quantum materials in which the electronic structure governs charge-carrier behaviour and reaction pathways through quantum-mechanical processes. To accelerate the discovery of superior photocatalytic quantum materials, we present a quantum-enhanced machine learning framework that integrates many-body electronic-structure descriptors with ensemble machine-learning algorithms. The framework explicitly incorporates quantum coherence lengths, electron–phonon coupling strengths, exciton binding energies, and spin–orbit coupling parameters obtained from density functional theory (DFT) and many-body perturbation theory GW approximation and the Bethe–Salpeter equation (GW-BSE) calculations. The database is based on a screened library of 8642 photocatalytic materials that have been experimentally characterised in H $ _2$ evolution reactions and contains 152 descriptors relating to electronic correlations, quantum confinement effects, and spin-dependent properties. Five-fold cross-validation yields $R^2 = 0.82$ and MAE $ = 0.28$ log units, $n$ 18% relative improvement in prediction accuracy ( $R^2$ : 0.82 vs 0.69) over a baseline model using only classical descriptors ( $R^2 = 0.69$ , MAE $ = 0.38$ log units). High-throughput screening of 45 000 hypothetical compositions yielded 214 candidate quantum materials; DFT and GW-BSE validation on representative subset of 23 of these candidates showed 72% agreement within quantum uncertainty bounds. quantum coherence shows a statistically significant association with photocatalytic charge separation, with optimal coherence lengths of 15–35 nm being strongly correlated with increased hydrogen-evolution production. Our findings indicate that many-body interactions, including exciton dissociation dynamics and quantum tunnelling contributions to interfacial charge transfer, are predictive of photocatalytic activity in emerging quantum materials.