Geometric Data Analysis-based Machine Learning For Two-dimensional Perovskite Design

The use of data-driven materials informatics, which integrates computational materials science with machine learning, has shown great potential in the discovery and design of new materials. This approach has been particularly successful in the development of halide perovskites, a semiconductor material with applications in optoelectronics. With the emergence of two-dimensional perovskites, also known as Perovskite 2.0, the potential for even more stable and tunable materials is on the horizon. This approach has been made possible by the Materials Genome Initiative, which combines artificial intelligence with materials science.