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