Multi-scale theoretical modeling with molecular simulation framework for fly ash-based high-performance concrete

Fly ash-based concrete models are currently largely empirical or homogenized and do not reflect the inherent properties of the materials, namely amorphous-crystalline heterogeneity, reactive interface dynamics, and defect evolution. Thus, the study leads to the advent of a complete multi-scale theoretical framework consisting of five unique approaches across structural and molecular scales. First and foremost, Hybrid Multiphase Microstructure Descriptor Modeling (HMMDM) reconstructs realistic 3D digital twins employing micro-CT, SEM/EDS, and PSD data for porosity prediction improvement by up to 15% by capturing interfacial topology. Second, the Quantum-Corrected Machine-Learned Interatomic Potential Mapping (QML IPM) develops system-specific force fields by coupling DFT data and Gaussian Approximation Potentials, reducing RMS force errors from 0.31 to 0.09 eV/Å, as well as improving the prediction of reactivity index by 22%. Third, the Topological Reaction Pathway Network Modeling (TRPNM) enables time-strength prediction with an error below 7% without being confined to the dynamics of graphs algorithms for modeling hydration and geopolymerization kinetics. Fourth, Fractal Defect Evolution Analysis using Molecular Simulation (FDEAMS) simulates stress induced crack development according to fractal mechanics, and an increase in tensile failure zone prediction capability by 20% is achieved. Finally, Dynamic Multi-Scale Simulation Coupling with Feedback Optimization (DMSCF) bridges a nano- and macro-scale design through iterative coupling of MD and FEM simulations, achieving real-time stiffness updates with a process lag of < 4% during analysis. The present study offers integrated approaches that would give unprecedented predictive fidelity on fly ash concrete and allow for optimal designs.