Understanding the relationship between multiscale morphology and electronic structure is key to designing organic semiconductors. Computational studies aimed at characterizing this multiscale relationship require a combination of quantum-chemical (QC) calculations, all-atom and coarse-grained (CG) molecular dynamics simulations, and back-mapping approaches. However, integrating and scaling these methodologies is extremely computationally intensive. Here, we utilize iterative Boltzmann inversion and electronic coarse-graining (ECG) to rigorously compute the morphology dependent electronic conductivity of liquid-crystalline molecules without the use of all-atom back mapping or <i>ad nauseum</i> QC calculations. The ECG model is established via the Deep Kernel Learning (DKL) framework to perform the mapping from CG coordinates to the all-atom electronic properties. We employ the DKL-ECG technique to model the morphology-dependent electronic structure of the liquid-crystalline molecular semiconductor, MeOPh-BTBT-8, accurately computing electron transport metrics across isotropic, nematic, and smectic liquid-crystalline phases. This fully CG approach to morphology and electron transport calculations opens a new pathway to address chemically specific organic semiconductors design using reduced sets of CG degrees of freedom.

Towards a fully coarse-grained treatment of morphology and electron transport in molecular semiconductors

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ACS Fall 2022

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