Machine learning discovery of high-temperature polymers.

ABSTRACT: To formulate a machine learning (ML) model to establish the polymer's structure-property correlation for glass transition temperature Tg , we collect a diverse set of nearly 13,000 real homopolymers from the largest polymer database, PoLyInfo. We train the deep neural network (DNN) model with 6,923 experimental Tg values using Morgan fingerprint representations of chemical structures for these polymers. Interestingly, the trained DNN model can reasonably predict the unknown Tg values of polymers with distinct molecular structures, in comparison with molecular dynamics simulations and experimental results. With the validated transferability and generalization ability, the ML model is utilized for high-throughput screening of nearly one million hypothetical polymers. We identify more than 65

SUBMITTER: Tao L 

PROVIDER: S-EPMC8085602 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature