Global discovery of stable and non-toxic hybrid organic-inorganic perovskites for photovoltaic systems by combining machine learning method with first principle calculations

Abstract

Traditional trial-and-error methods seriously restrict and hinder the searching of high-performance functional materials, especially when the search space is large. Rapid searching for advanced functional materials has always been a hot research topic, and attracted a lot of experimental and theoretical research attention. Here, by combining machine learning method with density functional theory (DFT) calculations, a target-driven method is proposed here to speed up the discovery of hidden hybrid organic-inorganic perovskites (HOIPs) for photovoltaic applications from 230808 HOIPs candidates which is almost two orders larger than previous studied. After imposing two criterions, i.e., charge neutrality condition and stability condition, on potential HOIPs candidates, followed by a machine learning (ML) screening, 686 orthorhombic-like HOIPs with proper bandgap are selected. In machine learning screening, ensemble learning using three ML models, including gradient boosting regression (GBR), supporting vector regression (SVR) and kernel ridge regression (KRR), are applied to predict the bandgap of 38086 HOIPs candidates. 132 stable and non-toxic (Cd-, Pb- and Hg-free) orthorhombic-like HOIPs are finally verified by DFT calculations with appropriate band gap for solar cells. In the present study, not only a series of unexplored stable and non-toxic HOIPs are discovered for further experimental synthesis, a new HOIPs database is constructed as well, thus beneficial to future functional material design.