Non-conjugated macromolecular radicals are conducting polymers with non-conjugated backbones bearing pendant stable radical sites. These polymers are promising materials for applications in all-organic memory or energy storage devices. A fundamental understanding of how the chemical structure impacts charge transport in these materials is still lacking. To address this, efficient computational approaches incorporating both mesoscale morphological features and electronic properties are required.<br/>We present an efficient computational approach that uses machine learning (ML) to predict electronic-structure information pertaining to charge transport at coarse-grained (CG) resolutions. ML models are trained on data obtained from quantum chemical calculations on conformations sampled from condensed-phase simulations at atomistic resolution. Predictions of energy levels, spin densities, and electronic couplings are investigated as a function of CG resolution. Trained ML models then enable electronic property predictions directly from CG polymer morphologies. We validate the approach by comparing to the standard methodology that requires backmapping the CG morphology to atomistic resolution and subsequent quantum chemical calculations. As such, this ML-assisted approach shows potential for drastically accelerating multiscale computational workflows, thereby enabling high-throughput modeling of radical polymers that takes into account both mesoscale and electronic properties.

Predicting electronic properties of macromolecular radicals at coarse-grained resolutions

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ACS Fall 2022

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