Characterizing peri-condensed Polybenzenoid Hydrocarbons using deep-learning Shany Erez, T. Weiss, A. Bronstein, R. Gershoni-Poranne Technion, Israel Peri-condensed polybenzenoid hydrocarbons (PBHs) are an important class of compounds that feature in many different functions. They serve as model systems for graphene nanoflakes and allow characterization of various molecular properties, including conductivity and magnetism. In this work, we established the first computationally-generated database of peri-condensed PBHs, COMPAS-2, and have begun to investigate the effects of peri-condensation on two molecular properties: relative energy (Erel) and HOMO-LUMO gap. The data generated and structural features identified will later serve to train deep-learning models for inverse design of novel functional PBHs. References 1. Weiss, T., Wahab, A., Bronstein, A., and Gershoni-Poranne, R.J. Org. Chem 2022 , Accepted 2. Wahab, A., Pfuderer, L., Paenurk, E. and Gershoni-Poranne, R. J. Chem. Inf. Model. 2022 , 62, 3704
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