AI‐Accelerated Design and Modeling of Organic Electrochemical Energy Materials: From Redox‐Active Molecules to Polymer Electrolytes

Abstract

Organic electrochemical energy materials (OEEMs) offer a vast design space for rechargeable batteries, redox‐flow batteries, supercapacitors, and mixed ionic–electronic devices, yet their performance depends on tightly coupled thermal, redox, transport, mechanical, morphological, and interfacial properties that can rarely be optimized independently. This review examines how artificial intelligence (AI) is reshaping the computational design and modeling of these materials, tracing the field's shift from small redox‐active molecules toward polymeric electrolytes and electrodes. We cover digital representations and data sources, data‐driven property prediction, machine‐learning interatomic potentials, generative molecular and polymer design, and large‐language model‐assisted (agentic) workflows. It is written for researchers entering the area from either direction, whether computational chemists curious about AI, or machine learning (ML) researchers new to electrochemical materials. Rather than an exhaustive survey, we give a selective snapshot of fast‐moving literature, using representative examples to draw out practical, transferable lessons, and we provide a concise checklist of best practices to help newcomers evaluate and report their own work before publication. We close with our perspective on where the field is heading, together with the persistent challenges of experimental data quality, validation, polymer representation, reproducibility, and modeling integration. The accompany online dataset catalog is maintained as a living community resource that evovles with the rapidly expanding OEEM data landscape.