Machine learning-powered nanoengineering of flexible, durable and scalable photoelectrode for efficient H2 production

A key challenge in renewable energy is achieving cost-effective, industrial-scale hydrogen production through water splitting, which necessitates highly efficient epitaxial heterojunction photoelectrodes. However, current fabrication methods fail to provide advanced materials with the essential trifecta of flexibility, durability, and energy efficiency, which are crucial for next-generation energy systems. Herein, we present machine learning (ML)-powered laser-writing fabrication parameters to engineer laser-induced graphene-Cu 2 O (bottom) /CuO (middle) /anisotropic carbon (top) [hereafter Cu 2 O/CuO/LIAC] heterostructures, merging ML with nanoscale precision. Using Bayesian optimization of 11 laser parameters (trained on physics-informed data), we achieve sub-3 % thickness control (0.18–2.11 % deviation) for epitaxial heterostructures, while gradient-boosted regression identifies laser-carbonization kinetics as the critical stability determinant. The resulting Cu 2 O/CuO/LIAC photocathode, comprising a current-collecting LIG layer, photoactive Cu 2 O/CuO bilayer, and anticorrosion/active electrolyte interface layer, exhibits exceptional performance: −3.2 mA cm −2 photocurrent (0 V vs. RHE) and 0.373 mmol g −1  h −1 H 2 yield under solar irradiation. Notably, it maintains ∼88 % photocurrent after 3000+ bending cycles (r = 5 mm), paired with robust recyclability and electrolyte stability. This ML-accelerated approach reduces optimization costs by 47 % versus trial-and-error methods, enabling scalable fabrication of flexible, stable heterostructures. By bridging AI-driven process control with laser precision, we advance next-generation photoelectrodes for sustainable H 2 energy.