# Accelerated Discovery of Halide Perovskite Materials via Computational Methods: A Review

## TL;DR

This review discusses how computational methods are speeding up the discovery of halide perovskite materials for optoelectronic applications.

## Contribution

The paper reviews how computational tools like high-throughput screening and machine learning are accelerating halide perovskite discovery.

## Key findings

- Computational methods enable efficient screening of large chemical spaces for halide perovskites.
- High-throughput screening and machine learning are accelerating the discovery of high-performance halide perovskite materials.
- Double and zero-dimensional perovskites are highlighted as promising structures identified through computational approaches.

## Abstract

Halide perovskites have gained considerable attention in materials science due to their exceptional optoelectronic properties, including high absorption coefficients, excellent charge-carrier mobilities, and tunable band gaps, which make them highly promising for applications in photovoltaics, light-emitting diodes, synapses, and other optoelectronic devices. However, challenges such as long-term stability and lead toxicity hinder large-scale commercialization. Computational methods have become essential in this field, providing insights into material properties, enabling the efficient screening of large chemical spaces, and accelerating discovery processes through high-throughput screening and machine learning techniques. This review further discusses the role of computational tools in the accelerated discovery of high-performance halide perovskite materials, like the double perovskites A2BX6 and A2BB′X6, zero-dimensional perovskite A3B2X9, and novel halide perovskite ABX6. This review provides significant insights into how computational methods have accelerated the discovery of high-performance halide perovskite. Challenges and future perspectives are also presented to stimulate further research progress.