Jan Henk Kamps

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.

High molar mass polycarbonate is synthesized via a solution transcarbonation of bis(methyl salicyl) carbonate and bisphenol‐A at temperatures between 60 and 160 °C without the removal of the condensate, allowing the incorporation of thermosensitive monomers into polycarbonate. Kinetic and equilibrium studies show that the polymerization is 20–30 times faster at 120 °C compared to 60 °C, whereas the equilibrium M_w increases from 11 × 10³ g mol⁻¹ at 120 °C to 16 × 10³ g mol⁻¹ at 60 °C. This polycondensation is characterized by very high equilibrium constants ranging from 0.8 × 10³ at 160 °C to 4.1 × 10³ at 60 °C, corresponding to standard enthalpies and entropies of polymerization: −19 kJ mol⁻¹ < ΔH⁰ < −11 kJ mol⁻¹ and 13 J mol⁻¹ K⁻¹ < ΔS⁰ < 28 J mol⁻¹ K⁻¹. Without removal of the condensate, the system is shown to be dynamic and completely reversible when changing the temperature. Good predictability of this polycondensation is reported, where only at very low starting monomer concentrations, the formation of cyclics leads to deviations from the predicted behavior.

To achieve good mechanical properties of carbon fibre-reinforced polycarbonate composites, the fibre-matrix adhesion must be dialled to an optimum level. The electrolytic surface treatment of carbon fibres during their production is one of the possible means of adapting the surface characteristics of the fibres. The production of a range of tailored fibres with varying surface treatments (adjusting the current, potential, and conductivity) was followed by contact angle, inverse gas chromatography and X-ray photoelectron spectroscopy measurements, which revealed a significant increase in polarity and hydroxyl, carboxyl, and nitrile groups on the fibre surface. Accordingly, an increase in the fibre-matrix interaction indicated by a higher interfacial shear strength was observed with the single fibre pull-out force-displacement curves. The statistical analysis identified the correlation between the process settings, fibre surface characteristics, and the performance of the fibres during single fibre pull-out testing.

Activated carbonates facilitate the preparation of polycarbonates based on monomers that are unsuitable for traditional melt polymerization at high temperatures. Bis(methyl salicyl) carbonate (BMSC) clearly shows reactivity benefits over diphenyl carbonate in melt polymerization reactions, resulting in shorter reaction times and reduced heat exposure during polymerization. The increased reactivity enables the melt polymerization of a wide range of monomers, as demonstrated by two examples using volatile resorcinol and sterically hindered tert-butyl hydroquinone as monomers in the preparation of (co)polycarbonates.