Abstract 

Computational materials science has evolved beyond elucidating material properties to include sophisticated theoretical frameworks that predict new materials and phenomena and describe photophysical processes across various simulation scales. This advancement has led to diverse computational tools such as density functional theory (DFT), molecular dynamics (MD), and recently developed machine learning potentials (MLP). These methodologies often intersect, enriching interdisciplinary theoretical and computational materials research. Using these advanced methodologies, we investigate hybrid perovskite materials, focusing on how dimensionality, crystal facets, and chemical composition influence their stability and photophysical properties. We also highlight recent advancements in studying hot carrier cooling processes and manipulating organic spacers to enhance spin splitting in these materials. Our work demonstrates the potential of computational insights to drive the discovery and understanding of hybrid perovskites.