Vaidyanath Ramakrishnan

The development of next-generation polymers necessitates optimizing several key properties simultaneously, a task that is expensive and infeasible using traditional trial-and-error experimental approaches. A promising alternative is employing a combination of machine learning and physics-based tools to rapidly screen the polymer design space and provide suggestions of new polymers that meet the critical properties required for industrial applications. In this study, we introduce a comprehensive workflow that utilizes machine learning and molecular modeling approaches to design new polymers with the focus on improving five polymer properties: (1) glass transition temperature, (2) dielectric constant, (3) refractive index, (4) stress optic coefficient, and (5) linear coefficient of thermal expansion. Using a small dataset ( < 200 unique polymers), we developed quantitative structure-property relationships (QSPRs) models to accurately predict the experimental polymer properties for both homo- and co-polymer systems. We tested several ML algorithms and identified the best models for predicting these polymer properties, achieving test set R ² greater than 0.77 across all properties. We then explored new polymers by creating a library of over ∼10 000 homopolymers using R-group enumeration tools and applied the trained QSPR models to rapidly predict the five polymer properties. The predictions of QSPR models were used to create a multi-parameter optimization score, which helped downselect the large polymer space to ∼10 promising candidates. The properties of these selected polymer candidates were subsequently validated with classical molecular dynamics simulations and density functional theory, revealing a strong correlation with the QSPR model predictions. Finally, one of the top candidates was validated by experiments, which showed good agreement against QSPR and physics-based models. Our workflow underscores the power of combining data-driven and theoretical methods in the polymer design process given a small dataset size, offering a valuable resource for experimentalists looking to leverage computer-aided strategies in materials innovation.

Diffusion coefficientD, scaled using the Stokes–Einstein relation, for the dye AB540 in several polymer meltsversusratio of polymer relaxation timeτ_pto characteristic diffusion timeτ_D. Inset shows schematic of a particle with radiusRin a polymer melt with tube diametera.

Photochromic dyes have restricted use in rigid polycarbonates because of slow coloration and decoloration kinetics. In this study, it is shown that the decoloration kinetics of two photochromic dyes can be controlled by tuning the chain stiffness and free volume of the host matrix. The introduction of flexible moieties in rigid BPA‐based polycarbonate chain accelerates the decoloration of these dyes whereas a rigid co‐monomer delays the decoloration kinetics. Although T_g might be used as a parameter to improve photochromism in polymer matrices, dynamic mechanical analysis demonstrates that the decoloration kinetics of the dyes in host polymer matrices having similar T_g depends primarily on the secondary relaxations and, thus, on the polymer architecture. The effect of the co‐monomer type on the characteristic ratio is also discussed underlining the potential relationship between the free volume and chain stiffness. These results open the possibility to develop transparent or semitransparent photochromic materials based on tailor‐made co‐polycarbonates. © 2016 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2016, 54, 1593–1601