journal article·2026-07Open access

Beyond Descriptor‐Based AI Design: Sp 2 ‐Hybridized Branched Side Chains Enable Pre‐Aggregation–Driven Seeding Effects in Green‐Solvent‐Processed Organic Solar Cells

Seokhwan Jeong, Donghoo Won, Zhe Sun, Chihyung Lee, Jaewook Kim, Seunglok Lee, Sangjin Yang, Jieun Kim, Keonho Yoon, Dong Young Kim, Yongjoon Cho, Seung‐Jae Shin, Hee‐Seung Lee, Doo‐Hyun Ko, Changduk Yang

Advanced Energy Materials· Volume 16· Issue 26

Abstract

ABSTRACT Descriptor‐based artificial intelligence (AI) has emerged as a paradigm for molecular design in organic solar cells (OSCs); however, it inherently overlooks collective effects governed by bond hybridization, intermolecular coupling, and aggregation thermodynamics. Such effects are encoded at the solution stage, where pre‐aggregation of photoactive materials dictates nucleation pathways, phase separation, and molecular ordering during film formation. Herein, we introduce a YBOV non‐fullerene acceptor featuring sp 2 ‐hybridized branched side chains that exhibit an unprecedentedly strong solution‐state pre‐aggregation propensity. This behavior translates into highly ordered solid films with a densely packed crystalline microstructure, enabled by a thermodynamically stabilized core–terminal dimer. As a result, incorporation of YBOV into OSCs not only outperforms the benchmark L8‐BO‐based device, but also confers an effective nucleation seeding‐agent function across diverse host OSC platforms, delivering efficiencies of up to 19.67% via green‐solvent processing by alleviating the intrinsic current–voltage trade‐off. Machine‐learning predictions largely match experimental photovoltaic parameters with a slight upward bias, except for open‐circuit voltage​, which exhibits anomalous behavior driven by pre‐aggregation–driven seeding effects beyond descriptor‐based AI. This work establishes sp 2 ‐hybridized branched side chains as a new molecular design principle, introducing pre‐aggregation‐enabled seeding effects beyond AI prediction and providing a universal strategy for high‐performance OSCs.
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Research context

OpenAlex topics: Machine Learning in Materials Science · Organic Electronics and Photovoltaics · Perovskite Materials and Applications

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Energy Engineering School of Energy and Chemical Engineering Ulsan National Institute of Science and Technology (UNIST) 50 UNIST‐gil, Ulju‐gun Ulsan South Korea

Department of Chemistry Sungkyunkwan University Suwon Gyeonggi South Korea

Department of Chemistry and InnoCORE AI‐CRED Institute Korea Advanced Institute of Science and Technology (KAIST) 291 Daehak‐ro, Yuseong‐gu Daejeon South Korea

Department of Chemical Engineering Pohang University of Science and Technology Nam‐gu Pohang‐si South Korea

Graduate School of Carbon Neutrality Ulsan National Institute of Science and Technology (UNIST) 50 UNIST‐gil, Ulju‐gun Ulsan South Korea

UNIST InnoCORE AI‐Space Solar Initiative Ulsan National Institute of Science and Technology (UNIST) Ulsan South Korea

Funding

National Research Foundation of Korea · Ministry of Science and ICT, South Korea

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