The increasing societal need for energy storage, currently essentially relying on the lithium-ion battery technology, presents pressing challenges that include the full life cycle of the batteries, their carbon footprint, and their reliance on scarce metals. Among emerging technologies, organic-based batteries that are degradable or recyclable may offer metal-free solutions to energy storage. One example of such emerging battery technologies are solid-state devices that use nonconjugated radical-containing polymers. A fundamental understanding of how the chemical structure of such polymers impacts their charge transport performance is still lacking. To enable the rational design of such polymers, there is a need of accurate and efficient multiscale models that incorporate both mesoscale morphological features and electronic structure information.<br/><br/>Using our recently developed method that combines machine learning and coarse-grained modeling to predict electronic properties at large spatiotemporal scales, we investigate radical-containing polymers and their charge transport properties. Coarse-grained modeling allows to probe relevant polymeric material length- and timescales. At the same time, electronic structure information is retained, allowing for the retrieval of conformationally-averaged electronic property distributions as a function of material morphology and processing conditions. The impact of polymer backbone, radical sites, and solvent swelling are investigated. The derived morphology-electronic structure relationships inform the design of radical-containing polymers with improved characteristics for all-organic battery materials.

Morphology-electronic structure relationships in radical-containing polymers for all-organic batteries

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