Recent trend of using computational science tools in organic-inorganic hybrid perovskite materials research

Abstract

Organic-inorganic hybrid perovskite materials have been considered promising candidates for solar cells in the future, and computational science tools have been widely used in the research on the structure and various properties of these perovskite materials. Researchers have also focused on finding the composition of ions which they need for specific purposes and have discovered new candidates with better performance in the organic-inorganic hybrid perovskite materials family. In this review, notable computational ways to assist the organic-inorganic hybrid perovskite materials research in recent years, including First Principals calculations or Density Functional Theory (DFT) calculations, and machine learning tools, have been summarized and discussed. The review shows various applications for First Principals and DFT calculations in this area, and also highlights the prominent potential for machine learning tools in finding new perovskite material candidates for novel solar cells.