Accelerated Development of Novel Biomass-Based Polyurethane Adhesives via Machine Learning

2-Pyrone-4,6-dicarboxylic acid (PDC) can be produced on a large scale from lignin by transformed bacteria, and its use as a bifunctional monomer to synthesize biomass-based polymers has been reported. Recently, excellent adhesive properties of the resulting biomass-based polymers were also reported, but their performance has not yet been optimized. In this study, we focus on improving the adhesive properties of PDC-based polyurethanes (PUs) by combining experiments and machine learning (ML). We synthesized an initial data set of 25 adhesive samples from different polyols and isocyanates with different isocyanate-to-hydroxyl ratios (<i>r</i>). Adhesive strengths were measured after hot-pressing at varying temperatures (<i>T</i>_heat, °C) and durations (<i>t</i>_heat, h), following a Taguchi L25 orthogonal design. Gaussian process-based Bayesian optimization (BO) was employed to identify an optimal PDC-based PU adhesive as a function of polyol type, isocyanate type, <i>r</i> ratio, heating temperature, and time with an improved adhesive strength of 10.04 ± 1.26 MPa after only five iterations. This approach highlights the effectiveness of BO in guiding experimental conditions for an enhanced performance. Random Forest regression was also used as an alternative ML approach and supported the conclusions. Overall, this study demonstrates the potential of the BO in accelerating the development and optimization of novel adhesive materials.