A review of photovoltaic interface engineering: Linking classical physics-based numerical simulation to data-driven optimization

Photovoltaic efficiency and long-term device stability are heavily governed by interfacial dynamics, which impact charge carrier transport, energy band alignment, defect generation, and non-radiative recombination losses. Despite rapid breakthroughs in solar cell architectures and absorber materials, unfavorable band offsets and localized trap states remain persistent bottlenecks to maximizing power conversion efficiency. This study systematically examines the core physical mechanisms driving interface defects, Shockley–Read–Hall (SRH) recombination, band alignment engineering, and carrier extraction kinetics. Traditional interface optimization strategies, ranging from chemical and field-effect passivation to surface modifications and buffer layer engineering, are critically evaluated.  Furthermore, we assess the role of physics-based numerical solvers (including SCAPS and TCAD) in mapping interfacial electric fields, electronic energy bands, and recombination behaviours. Particular attention is dedicated to the integration of data-driven interface optimization strategies, detailing how machine learning accelerates material screening, defect modeling, and molecular passivation design. The integration of machine learning with physics-based simulations is highlighted, and this work outlines a technical roadmap to bypass current challenges in data sparsity and model interpretability, charting a course toward physics-informed machine learning for next-generation self-optimizing, high-efficiency solar architectures.