Prediction of the coefficient of linear thermal expansion for the amorphous homopolymers based on chemical structure using machine learning. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Gracheva, Ekaterina; Lambard, Guillaume; Samitsu, Sadaki; Sodeyama, Keitaro; Nakata, Ayako
• Is Part Of: Science and Technology of Advanced Materials: Methods. Volume 1:Issue 1(2021)
• Page Start: 213
• Page End: 224
• Publication Date: 2021-01-01
• Subjects: Thermal expansion -- homopolymers -- machine learning -- polymer informatics -- cheminformatics -- fingerprint -- random forest; Materials data analysis and utilization
• DOI: 10.1080/27660400.2021.1993729
• Languages: English
• ISSNs: 2766-0400

Abstract

ABSTRACT: The coefficient of thermal expansion (CTE) is an industrially crucial macroscopic property of polymers. Yet, there is no structure-based model expressing it with sufficient accuracy. In this work, we present two data-driven predictive models for the linear CTE of amorphous homopolymers in the glassy state based solely on chemical structure, showing consistent predictions. The first model is built with the SMILES-X software and is based on the simplified molecular-input line-entry system (SMILES) of polymer's repeating unit as input. The second model is built with a random forest trained on extended-connectivity fingerprints of repeating units. Both models are trained on 106 experimental data samples taken from the PoLyInfo database. The out-of-sample prediction shows a root-mean-square error of 2.65 ± 0.09 × 10 –5 K –1 (2.58 ± 0.09 × 10 –5 K –1 ), a mean absolute error of 1.71 ± 0.06 × 10 –5 K –1 (1.61 ± 0.06 × 10 –5 K –1 ) and a coefficient of determination of 0.62 ± 0.03 (0.64 ± 0.03) for SMILES-X (random forest). Additionally, the models are validated experimentally using a lab-prepared sample with good agreement ( p -value ≫ for both models). The attention mechanism, incorporated into SMILES-X, points out salient SMILES substructures, and the resulting maps suggest that the model takes decisions on a chemically interpretable basis. Abbreviations: SMILES; CTE; CLTE; CVTE Graphical abstract: uf0001