Machine Learning‐Guided Design of a Flexible Highly Conductive Additive‐Free Polymer Cathode

ABSTRACT Organic cathode materials (OCMs) are promising sustainable alternatives to inorganic counterparts for next‐generation batteries, yet their widespread application is largely hindered by intrinsically low electrical conductivity (below 10 −6 S cm −1 ) and material dissolution. The vast chemical space for exploration complicates the discovery of optimal OCMs. In this work, we utilized a machine learning (ML)‐based discovery process with a pretrained transformer model in ZINC organic molecules database, yielding a couple of potential high‐performance OCMs candidates, including isoindigo‐type redox units. The output of such efficient screening inspires the design of poly‐benzodifurandione (PBFO) as a free‐standing cathode material for high‐performance Li‐ion and Na‐ion storage. The flexible PBFO film exhibits a breakthrough conductivity of 5.9×10 2 S cm −1 , setting a new benchmark for additive‐free organic cathodes. The neat PBFO cathodes achieve a reversible capacity of 262 mAh g −1 averaging at 2.5 V versus Li + /Li at 25 mA g −1 , delivering a high electrode‐level energy density of 655 Wh kg −1 , among the highest reported for OCMs. This work provides the first flexible, high‐conductivity organic cathodes without conductive additives and binders, opening a new direction toward viable organic batteries.