Computational Modeling of Defects and Transport in Perovskite Nanostructures

As a prominent family of nanostructures, perovskites exhibit exceptional optoelectronic properties, primarily due to lead halide elements. However, defects such as vacancies, interstitials, and grain boundaries significantly impact their performance by altering charge and ion transport mechanisms. Understanding these defects and their effects is crucial for enhancing the performance of perovskite-based devices. In this context, computational modeling plays a vital role, allowing researchers to simulate defect formation and analyze their influence on the material’s properties at an atomic level. Defects in perovskite nanostructures and charge transport processes are commonly studied using methods like density functional theory (DFT), molecular dynamics (MD), and machine learning (ML) techniques. This chapter delves into the interconnected effects of defects and transport mechanisms, including charge carrier mobility, ion migration, and phonon transport, which affect thermal conductivity and device stability. Furthermore, it reviews recent advances in computational methods, emphasizing improvements in high-throughput techniques, such as ML-based approaches, that accelerate materials screening for specific properties or defect states. The manipulation of perovskite defect densities and types is highlighted as a critical design principle for improving device efficiency through defect engineering. The chapter concludes by addressing existing challenges in experimental validation and computational limitations while proposing new approaches that integrate theoretical and experimental work. These advancements could lead to defect-tolerant, high-performance perovskite nanostructures for various technological applications.