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Abstract Geopolymers, a class of alkali‐activated binders, are studied as sustainable alternatives to Ordinary Portland Cement due to their potential for CO₂emission reduction. However, the critical relationship between early‐age reaction kinetics, the development of material properties, and evolving chemical structure remains insufficiently explored, primarily because of the complexity of the underlying chemical reactions and the wide variety of geopolymer chemistries. To address this, we investigate the mechanism of early‐age (<72 h) strength development of a model metakaolin geopolymer by measuring curing kinetics using isothermal calorimetry, material property development via rheology, and chemical coordination at distinct extents of reaction via²⁹Si and²⁷Al NMR. A novel approach of collecting solid‐state²⁹Si and²⁷Al NMR spectra at low temperature (−17°C) successfully quenches the geopolymer reaction, allowing for spectrum collection at a desired extent of reaction despite long²⁹Si NMR spectrum collection times. Applying the Avrami kinetic model to deconvoluted calorimetry data enables independent analysis of dissolution and polycondensation/crosslinking reactions. From these data, the gel reaction product mass fraction is estimated, revealing an exponential relationship with the storage modulus in the activated metakaolin slurry. This study provides new insights into the interconnected dynamics of molecular chemistry, reaction kinetics, rheology, and strength development, offering a semi‐empirical framework for understanding property evolution in geopolymers more broadly. 

Predicting the response of complex fluids to different flow conditions has been the focal point of rheology and is generally done via constitutive relations. There are, nonetheless, scenarios in which not much is known from the material mathematically, while data collection from samples is elusive, resource-intensive, or both. In such cases, meta-modeling of observables using a parametric surrogate model called multi-fidelity neural networks (MFNNs) may obviate the constitutive equation development step by leveraging only a handful of high-fidelity (Hi-Fi) data collected from experiments (or high-resolution simulations) and an abundance of low-fidelity (Lo-Fi) data generated synthetically to compensate for Hi-Fi data scarcity. To this end, MFNNs are employed to meta-model the material responses of a thermo-viscoelastic (TVE) fluid, consumer product Johnson’s® Baby Shampoo, under four flow protocols: steady shear, step growth, oscillatory, and small/large amplitude oscillatory shear (S/LAOS). In addition, the time–temperature superposition (TTS) of the material response and MFNN predictions are explored. By applying simple linear regression (without induction of any constitutive equation) on log-spaced Hi-Fi data, a series of Lo-Fi data were generated and found sufficient to obtain accurate material response recovery in terms of either interpolation or extrapolation for all flow protocols except for S/LAOS. This insufficiency is resolved by informing the MFNN platform with a linear constitutive model (Maxwell viscoelastic) resulting in simultaneous interpolation and extrapolation capabilities in S/LAOS material response recovery. The roles of data volume, flow type, and deformation range are discussed in detail, providing a practical pathway to multifidelity meta-modeling of different complex fluids.