Work

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

Year: 2026

Type: article

Abstract: 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 (E ads <−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.

Source: Advanced Science

DOI: 10.1002/advs.75486

Authors: Jing Zhang, Ziyuan Li, Shan Gao, Zhen Zhu, Jing Wang, Xiangmei Duan

Institution: Ningbo University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: gold

Estimated APC paid: $6,730