Abstract
We introduce HAPPY (Hierarchically ed rePeat unit of PolYmers), a string representation for polymers, designed to efficiently encapsulate essential polymer structure features for property prediction. HAPPY assigns single constituent elements to groups of sub-structures and employs grammatically complete and independent connectors between chemical linkages. Using a limited number of datapoints, we trained neural networks utilizing both HAPPY and conventional SMILES encoding of repeated unit structures and compared their performance in predicting five polymer properties: dielectric constant, glass transition temperature, thermal conductivity, solubility, and density. The results showed that the HAPPY-based network could achieve higher prediction R-squared score and two-fold faster training times. We further tested the robustness and versatility of HAPPY-based network with an augmented training dataset. Additionally, we present topo-HAPPY (Topological HAPPY), an extension that incorporates topological details of the constituent connectivity, leading to improved solubility and glass transition temperature prediction R-squared score.
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