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- Title
Enhancing deep learning predictive models with HAPPY (Hierarchically Abstracted rePeat unit of PolYmers) representation.
- Authors
Ahn, Jihun; Irianti, Gabriella Pasya; Choe, Yeojin; Hur, Su-Mi
- Abstract
We introduce HAPPY (Hierarchically Abstracted 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.
- Subjects
PREDICTION models; POLYMERS; GLASS transition temperature; POLYMER structure; PERMITTIVITY; DIELECTRIC properties; THERMOPHYSICAL properties
- Publication
NPJ Computational Materials, 2024, Vol 10, Issue 1, p1
- ISSN
2057-3960
- Publication type
Article
- DOI
10.1038/s41524-024-01293-8