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 <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msub><mi>T</mi> <mi>g</mi></msub> </mrow> </math> , 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 <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msub><mi>T</mi> <mi>g</mi></msub> </mrow> </math> values using Morgan fingerprint representations of chemical structures for these polymers. Interestingly, the trained DNN model can reasonably predict the unknown <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msub><mi>T</mi> <mi>g</mi></msub> </mrow> </math> 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 65,000 promising candidates with <math xmlns="http://www.w3.org/1998/Math/MathML"> <mrow><msub><mi>T</mi> <mi>g</mi></msub> </mrow> </math> > 200°C, which is 30 times more than existing known high-temperature polymers (∼2,000 from PoLyInfo). The discovery of this large number of promising candidates will be of significant interest in the development and design of high-temperature polymers.