Machine Learning-enhanced Copper (I) Thiocyanate-based Perovskite-silicon Tandem Solar Cells: Optimization Strategies for Enhanced Effi ciency and Stability

This paper investigates the role of machine learning (ML) techniques in advancing CuSCN-based perovskite tandem solar cells (PTSCs), addressing critical challenges such as power conversion efficiency, scalability, and long-term operational stability. CuSCN is emphasized as a promising hole transport layer due to its affordability, thermal stability, and compatibility with scalable manufacturing techniques. Leveraging ML-driven frameworks , the study optimizes key parameters, enhances layer uniformity, reduces defect density, and refines interface engineering, achieving significant improvements compared to conventional methods . Results demonstrate that ML-based optimization facilitates power conversion efficiencies exceeding 29% under controlled conditions while offering precise predictions of long-term performance and degradation mechanisms. This outcome establishes a significant benchmark for integrating CuSCN into PTSCs while maintaining environmental and economic sustainability. Furthermore, the study underscores ML’s capability in tailoring complex device architectures and minimizing the experimental efforts required to achieve optimal configurations. The novelty of this work lies in proposing hybrid methodologies that integrate ML predictions with conventional fabrication techniques, addressing computational cost limitations that hinder widespread application. Additionally, the study contributes to expanding open-access datasets and lightweight ML models, expanding access to optimization tools in resource-limited environments. This research bridges critical gaps in previous studies by presenting a comprehensive framework for material and device optimization while providing scalable solutions to expedite PTSC commercialization. These findings position CuSCN-based PTSCs as a transformative, sustainable alternative for advancing renewable energy technologies and meeting global energy demands.