Decoupling Intrinsic Molecular Efficacy from Platform Effects: An Interpretable Machine Learning Framework for Unbiased Perovskite Passivator Discovery

TL;DR

This paper introduces an interpretable machine learning framework that disentangles intrinsic molecular efficacy from platform effects, enabling unbiased discovery of effective perovskite passivators through a combined data-driven and first-principles approach.

Contribution

The study presents a novel, generalizable ML framework that isolates intrinsic molecular effects, guiding the discovery of superior passivators with high confidence and broad applicability.

Findings

Identified key descriptors like hydrogen bond acceptor strength and electrostatic potential difference.

Screened over 121 million compounds leading to five promising candidates.

First-principles calculations confirmed strong chemisorption and favorable energetics.

Abstract

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.