The effect of additive engineering and machine learning on high performance perovskite solar cells

Additive engineering has emerged as a powful strategy for enhancing the efficiency and stability of perovskite solar cells (PSCs), enabling precise control over crystallization kinetics, defect passivation, interfacial energetics, and long-term environmental stability. By controlling nucleation and crystal growth, and thereby optimizing film morphology, additives effectively suppress non-radiative recombination and ion migration, addressing key challenges in the path toward commercialization. However, the conventional discovery process remains largely empirical and time-consuming. The integration of machine learning (ML) offers a promising avenue for data-driven screening, rational molecular design, and accelerated optimization of additive systems. ML models trained on experimental datasets and augmented with density functional theory (DFT) and molecular dynamics (MD) simulations can predict interactions between additives and perovskites, identify performance-determining descriptors, and guide the discovery of novel functional molecules. This review systematically outlines the multifaceted roles of additives in PSCs, from crystallization regulation to interfacial stabilization. We further highlight the synergy between ML and additive engineering, emphasizing its potential to establish a predictive, intelligent framework for next-generation photovoltaic materials.