Explainable deep learning elucidates orbital–spin synergy in multi-field-responsive Pt-BiFeO3 for efficient photocatalytic H2 production

Electrons serve as fundamental mediators of energy conversion in photocatalytic systems, governing light harvesting, charge excitation, carrier migration, and surface redox processes that ultimately dictate the efficiency of solar-driven chemical transformations. However, precise manipulation of electronic states in photocatalytic systems remains challenging because the intrinsic complexity of electron configuration and dynamic evolution restricts the optimization of catalytic performance. Herein, we demonstrate that orbital–spin synergy deciphered by explainable deep learning governs multi-field responsiveness of Pt-doped BiFeO3 for efficient photocatalytic hydrogen production. Orbital–spin synergy reconstructs electronic states via Pt 5d–Fe 3d–O 2p hybridization and spin redistribution promoting charge separation across multiple external fields. A multi-property density functional theory database covering 29 transition-metal dopant elements and 58 site-specific BiFeO3 doping configurations was established to screen BiFeO3 using electronic, optical, dielectric, piezoelectric, and spin descriptors. Pt-doped BiFeO3 achieved the highest screening score while structural and spectroscopic characterizations confirmed successful Pt incorporation with preserved lattice integrity. Multi-field responsiveness was evaluated through carrier-dynamics measurements and photocatalytic hydrogen evolution under coupled light, magnetic, mechanical, and polarization fields. Suppressed electron–hole recombination affords superior hydrogen evolution activity, prolonged carrier lifetime, reduced charge-transfer resistance, and outstanding cycling stability. This work reveals orbital–spin synergy as an effective pathway for electronic-state engineering, offering new guidance for the development of photocatalytic systems with efficient solar energy conversion.