Machine-Learning-Guided Optimization of Rapid-Lamp-Annealed Hematite Photoanodes for Solar Water Splitting

Abstract

Hematite (α-Fe2O3) photoanodes for solar water splitting suffer from poor charge transport and significant sample-to-sample irreproducibility. Here we show that rapid lamp annealing (RLA), in which a graphite susceptor enables rapid volumetric heating, produces hematite films with substantially higher and more reproducible photocurrents than conventional furnace annealing, eliminating the population of inactive electrodes that arises under prolonged calcination. Across 171 solution-deposited samples, RLA films achieved an average photocurrent of ∼0.98 mA cm−2 at 1.23 V vs RHEapproximately four times higher than furnace-annealed sampleswith all RLA samples remaining active. PEIS and XRD descriptors were analyzed using various machine learning models with hierarchical feature clustering and SHapley Additive exPlanations (SHAP), achieving a test R2 of 0.84. SHAP identified bulk and interfacial resistances (R2, R3) and bulk capacitance (C2) as dominant descriptors, with RLA samples consistently occupying the low-resistance, high-C2 region of descriptor space. A bisected-substrate experiment confirmed that this improvement is intrinsic to the annealing method rather than precursor variability. Scanning electron microscopy confirmed that RLA preserves the rod-like nanoparticle morphology of the β-FeOOH precursor, whereas furnace annealing drives grain coalescencedirectly accounting for the improved charge-transport descriptors. These results establish RLA as a mechanistically understood route to high-performance hematite photoanodes and demonstrate that the ML-SHAP framework provides physically interpretable process guidance from datasets of modest size.