Division of labor among self‐assembled monolayer components at nickel oxide interfaces: A DFT‐Machine learning framework for inverted perovskite solar cells

The rational design of self‐assembled monolayers (SAMs) for nickel oxide (NiO x )/perovskite interfaces is challenged by the vast molecular chemical space. Here, we integrate literature mining, density functional theory calculations, and machine learning to establish a data‐driven framework for SAM design. SAMs are deconstructed into anchoring groups, π‐conjugated backbones, and terminal groups to quantify their distinct roles. Statistical analysis reveals that conjugated backbones dominate energy‐level variation, anchoring groups contribute most strongly to binding, and interfacial charge transfer depends on both anchoring chemistry and anchoring‐by‐backbone interactions. Machine‐learning models trained on DFT‐derived properties enable structure–property analysis and candidate prioritization, with uncertainty assessed through repeated nested cross‐validation and statistical testing. External evaluation supports binding‐energy transferability within the sampled single‐anchor regime while revealing limitations for dual‐anchor molecules. An exploratory PCA‐derived descriptor further summarizes multidimensional interfacial properties. This framework provides mechanistic insights and a computational strategy for prioritizing SAM candidates for subsequent experimental evaluation.