Advancing organic photovoltaic materials by machine learning-driven design with polymer-unit fingerprints
Xiumin Liu, Xinyue Zhang, Ye Sheng, Zihe Zhang, Pan Xiong, Xue‐Hai Ju, Junwu Zhu, Caichao Ye
Abstract
To enhance the power conversion efficiency (PCE) of organic photovoltaic (OPV) cells, the identification of high-performance polymer/macromolecule materials and understanding their relationship with photovoltaic performance before synthesis are critical objectives. In this study, we developed five algorithms using a dataset of 1343 experimentally validated OPV NFA acceptor materials. The random forest (RF) algorithm exhibited the best predictive performance for material design and screening. Additionally, we explored a newly developed polymer/macromolecule structure expression, polymer-unit fingerprint ( PUFp ), which outperformed the molecular access system (MACCS) across diverse machine learning (ML) algorithms. PUFp facilitated the interpretability of structure-property relationships, enabling PCE predictions of conjugated polymers/macromolecules formed by the combination of donor (D) and acceptor (A) units. Our PUFp -ML model efficiently pre-evaluated and classified numerous acceptor materials, identifying and screening the two most promising NFA candidates. The proposed framework demonstrates the ability to design novel materials based on PUFp -ML-established feature/substructure-property relationships, providing rational design guidelines for developing high-performance OPV acceptors. These methodologies are transferable to donor materials, thereby supporting accelerated material discovery and offering insights for designing innovative OPV materials.