Abstract Organic redox-active molecules and solid-state organic materials present a uniquely tunable platform for next-generation energy storage technologies. Their molecular diversity enables precise control over redox energetics, solubility, transport, and long-term stability—yet the breadth of the organic chemical space and the complexity of reactivity have hindered systematic discovery. Recent advances in multi-fidelity quantum chemistry approaches, machine learning, graph neural networks, large language models (LLMs), and agentic autonomous workflows are redefining how we identify and optimize organic materials for both flow and solid-state battery systems. This perspective synthesizes developments across computational chemistry, machine learning, and autonomous science, integrating contributions from our research group and others in computational discovery of organic materials using multiscale modeling, machine learning, and foundation models. By highlighting key challenges, foundational datasets, and emerging opportunities in closed-loop discovery, we outline a forward-looking roadmap for accelerating organic battery innovation through AI-guided materials design. Highlights We outline the role of advanced simulations, emerging AI techniques in closed-loop materials design and discovery for organic energy storage. Discussion We document recent advances in multi-fidelity quantum chemistry, Physics-based AI, large language models (LLMs), and agentic autonomous workflow-based approaches to accelerate organic battery innovation. Recent computational developments point toward a new era of digitally driven, closed-loop materials discovery, in which computation and AI act not merely as supporting tools, but as the central engines of innovation for organic battery technologies. Graphical abstract
Harb et al. (2026) studied this question.