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

Johannes Teunissen (1, 2, 3) , Fabiana da Pieve (1)

1 BIRA-IASB - Belgian Institute for Space Aeronomy / Institut d'Aéronomie Spatiale de Belgique
2 IEMN - Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520
3 PHYSIQUE - IEMN - Physique - IEMN

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 with ion migration. The specific role of noncovalent 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 noncovalent molecular bonds, can guide the design of perovskites with suppressed ion migration. The results unveil the distinct nature of different noncovalent 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 formamidinium compared to methylammonium, which shows to be different from previous arguments in the literature, and the reasons for the improved stability given by the halogen F and explain the exceptional case of overall stronger bonds for guanidiunium. Within the stability boundaries given by the Goldschmidt factor, the found descriptors give an all-in-one picture of noncovalent interactions which provide more stable configurations, also including interactions other than H bonds. Such descriptors are more informative than previously used quantities and can be used as a universal input to better inform new machine learning studies.

Domaines: Sciences de l'ingénieur [physics]

Johannes Teunissen, Fabiana da Pieve. Molecular Bond Engineering and Feature Learning for the Design of Hybrid Organic–Inorganic Perovskite Solar Cells with Strong Noncovalent Halogen-Cation Interactions. Journal of Physical Chemistry C, 2021, 125 (45), pp.25316-25326. ⟨10.1021/acs.jpcc.1c07295⟩. ⟨hal-03549085⟩