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Hybrid organic-inorganic perovskites are exceedingly interesting candidatesfor new solar energy technologies, for both ground-based and spaceapplications. However, their large-scale production is hampered by the lack oflong-term stability, mostly associated to ion migration. The specific role ofnon-covalent bonds in contributing to the stability remains elusive, and incertain cases controversial. Here, we perform an investigation on a largeperovskite chemical space via a combination of first-principles calculationsfor the bond strengths and the recently developed Sure Independent Screeningand Sparsifying Operator (SISSO) algorithm. The latter is used to formulatemathematical descriptors that, by highlighting the importance of specificnon-covalent molecular bonds, can guide the design of perovskites withsuppressed ion migration. The results unveil the distinct nature of differentnon-covalent interactions, with remarkable differences compared to previousarguments and interpretations in the literature on the basis of smallerchemical spaces. In particular, we clarify the origin of the higher stabilityoffered by FA compared to MA, which shows to be different from previousarguments in the literature, the reasons of the improved stability given by thehalogen F, and explain the exceptional case of overall stronger bonds forGuanidiunium. The found descriptors reveal the criteria that, within thestability boundaries given by the Goldschmidt factor, give an all-in-onepicture of non-covalent interactions which provide the more stableconfigurations, also including interactions other than H-bonds. Suchdescriptors are more informative than previously used quantities and can beused as universal input to better inform new machine learning studies.

journal of physical chemistry. c./journal of physical chemistry. c

Molecular Bond Engineering and Feature Learning for the Design of Hybrid  Organic-Inorganic Perovskites Solar Cells with Strong Non-Covalent  Halogen-Cation Interactions