Rational design of interface passivators for perovskite solar cells is hindered by the entanglement of intrinsic molecular efficacy with extrinsic platform-dependent performance - a confounding factor that obscures true chemical advances. Here, we present a generalizable, interpretable machine learning framework that decouples these effects via an asymptotic saturation model, enabling unbiased discovery of molecules with genuine intrinsic gains. Trained on a curated dataset of 240 experimental entries, our model identifies hydrogen bond acceptor strength and electrostatic potential difference as key descriptors. Guided by these insights, we screened >121 million PubChem compounds using a hierarchical strategy integrating diversity clustering and uncertainty quantification. Five dual-functional candidates (e.g., TDZ-S, TZC-F) are identified, exhibiting superior predicted efficacy (surpassing experimental benchmarks) and high confidence. First-principles calculations confirm strong chemisorption (Eads<-1.7 eV), net electron donation, and optimized interfacial energetics. Crucially, our closed-loop "data-interpretation-screening-verification" pipeline establishes a transferable paradigm for rational materials design, extendable to other optoelectronic interfaces beyond perovskites.
@article{arxiv.2603.02717,
title = {Decoupling Intrinsic Molecular Efficacy from Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery},
author = {Jing Zhang and Ziyuan Li and Shan Gao and Zhen Zhu and Jing Wang and Xiangmei Duan},
journal= {arXiv preprint arXiv:2603.02717},
year = {2026}
}