A Theory-Guided Machine Learning and Molecular Dynamics Approach for Characterizing Fast-Curing Polyurethane Systems

Fast-curing polyurethane (PU) systems are attractive for high-throughput manufacturing, but quantifying cure kinetics, gelation, and cure-dependent glass transition temperature (Tg) is difficult, especially at a low degree of cure (<i>DoC</i>). Here, a fast-reacting BASF PU formulation was studied using non-isothermal differential scanning calorimetry (DSC) at multiple heating rates, rheometry at 50 °C, and molecular dynamics (MD) simulations to extend Tgα in the low-<i>DoC</i> regime. DSC provided reaction enthalpy and conversion histories, and Kamal-Sourour (KS) parameters were identified by robust nonlinear fitting, reproducing conversion and curing rate profiles (<i>R</i>² > 0.99 and >0.95). Rheology indicated gelation between 475 and 625 s (<i>DoC</i> ≈ 0.53), and DSC-based Tg at uncured, gelation, and fully cured states, established the experimental Tg trend. MD (LAMMPS) with topological crosslinking and NPT thermal scans extracted Tg from density-temperature slopes at selected <i>DoC</i> points. Experimental and MD Tg data were fused with Gaussian process regression constrained by the DiBenedetto relationship (5-fold cross-validation), giving <i>λ</i> ≈ 0.29 and confidence intervals. This framework links kinetics, gelation, and Tg evolution for fast-curing PU and identifies the low-<i>DoC</i> region as the main source of uncertainty.