Molecular Bond Engineering and Feature Learning for the Design of Hybrid Organic–Inorganic Perovskite Solar Cells with Strong Noncovalent Halogen–Cation Interactions

Abstract

Hybrid organic-inorganic perovskites are exceedingly interesting candidates for new solar energy technologies, for both ground-based and space applications. However, their large-scale production is hampered by the lack of long-term stability, mostly associated to ion migration. The specific role of non-covalent bonds in contributing to the stability remains elusive, and in certain cases controversial. Here, we perform an investigation on a large perovskite chemical space via a combination of first-principles calculations for the bond strengths and the recently developed Sure Independent Screening and Sparsifying Operator (SISSO) algorithm. The latter is used to formulate mathematical descriptors that, by highlighting the importance of specific non-covalent molecular bonds, can guide the design of perovskites with suppressed ion migration. The results unveil the distinct nature of different non-covalent interactions, with remarkable differences compared to previous arguments and interpretations in the literature on the basis of smaller chemical spaces. In particular, we clarify the origin of the higher stability offered by FA compared to MA, which shows to be different from previous arguments in the literature, the reasons of the improved stability given by the halogen F, and explain the exceptional case of overall stronger bonds for Guanidiunium. The found descriptors reveal the criteria that, within the stability boundaries given by the Goldschmidt factor, give an all-in-one picture of non-covalent interactions which provide the more stable configurations, also including interactions other than H-bonds. Such descriptors are more informative than previously used quantities and can be used as universal input to better inform new machine learning studies.