Δ-Machine learning-driven discovery of double hybrid organic–inorganic perovskites

Abstract

Double hybrid organic–inorganic perovskites (DHOIPs) with excellent optoelectronic properties and low production costs are promising in photovoltaic applications. However, DHOIPs still have not been investigated thoroughly, due to their structural complexities. In this work, an accelerated discovery of DHOIPs has been realized by combining machine learning (ML) techniques, high-throughput screening, and density functional theory calculations. Different from the previous works, the anisotropy of organic cations of DHOIPs was first considered, and Δ-machine learning (Δ-ML), which uses low-level calculations as a baseline to predict properties of high-level methods, was used in high-throughput of DHOIPs to further improve the accuracy of ML models. 19 promising DHOIPs with appropriate bandgaps for solar cells were screened out from 78 400 DHOIPs and verified by performing HSE06 calculations. This work demonstrates an effective method for predicting and discovering hidden novel photovoltaic materials.