Abstract
Polyamides are often used for their superior thermal, mechanical, and chemical properties. They form a diverse set of materials that have a large variation in properties between linear to aromatic compounds, which renders the traditional quantitative structure–property relationship (QSPR) challenging. We use extended connectivity fingerprints (ECFP) and traditional QSPR fingerprints to develop machine learning models to perform high fidelity prediction of glass transition temperature (), melting temperature (), density (), and tensile modulus (). The non-linear model using random forest is in general found to be more accurate than linear regression; however, using feature selection or regularization, the accuracy of linear models is shown to be improved significantly to become comparable to the more complex nonlinear algorithm. We find that none of the models or fingerprints were able to accurately predict the tensile modulus , which we hypothesize is due to heterogeneity in data and data sources, as well as inherent challenges in measuring it. Finally, QSPR models revealed that the fraction of rotatable bonds, and the rotational degree of freedom affects polyamide properties most profoundly and can be used for back of the envelope calculations for a quick estimate of the polymer attributes (glass transition temperature, melting temperature, and density). These QSPR models, although having slightly lower prediction accuracy, show the most promise for the polymer chemist seeking to develop an intuition of ways to modify the chemistry to enhance specific attributes.
Highlights
Polyamides are a family of polymers that contain repeat units linked by amide groups.They are often prepared by hydrolytic polymerization, anionic polymerization, or solid phase synthesis
We focus on predicting density, tensile modulus, glass transition temperature, and melting temperature for polyamides with a goal of providing heuristic methods for property prediction for chemists
Through comparison of the RMSEs of model predictions of Tg, depicted in Figure 2a, we first observe that simple linear regression results in large RMSE, error that is several orders of magnitude larger than the property values (e.g., >1010 ◦ C for Tg, which are typically
Summary
Polyamides are a family of polymers that contain repeat units linked by amide groups. They are often prepared by hydrolytic polymerization, anionic polymerization, or solid phase synthesis. The diversity and favorable properties of polyamides have resulted in them finding applications [4] in flexible packaging, automotive industries, and garments [5]. This diversity of attributes makes developing novel polyamides to achieve the desired material properties a challenging task. Polyamides exhibit strongly non-linear and anisotropic structure property relations, which makes a targeted design of the experiment to systematically study the correlations between structure and property difficult.
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