Enhancing deep learning predictive models with HAPPY(Hierarchically Abstracted rePeat unit of PolYmers)representation  

作　　者：Jihun Ahn Gabriella Pasya Irianti Yeojin Choe Su-Mi Hur 

机构地区：[1]Department of Polymer Engineering,Graduate School,Chonnam National University,Gwangju 61186,Republic of Korea [2]School of Polymer Science and Engineering,Chonnam National University,Gwangju 61186,Republic of Korea

出　　处：《npj Computational Materials》2024年第1期2112-2122,共11页计算材料学(英文)

基　　金：supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2018R1A5A 1025224);by the Technology Innovation Program(20016176)funded By the Ministry of Trade,Industry&Energy(MI,Korea).

摘　　要：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.

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分 类 号：G63[文化科学—教育学]