Interfacial Insights and Remediation Strategies through Molecular Dynamics Probing of Nanoparticle Interactions in Cementitious Matrices: A Review

Enhancing the performance and longevity of cementitious nanocomposites requires a comprehensive understanding of the interfacial interactions between nanomaterials and cement hydration products. Despite considerable experimental advancements, the fundamental mechanisms at the nanoscale remain inadequately understood concerning the effects of nanoparticle dispersion, bonding, and functionalization on the bulk mechanical properties. This review addresses this critical gap by synthesizing insights from over 200 peer-reviewed studies and integrating experimental data, molecular dynamics (MD) simulations, and machine learning (ML)-augmented multiscale models to establish a comprehensive bottom-up framework. It focuses on how nanomaterials such as carbon nanotubes, nanosilica, graphene oxide, nano-Al2O3, nano-Fe2O3, and nano-TiO2 modulate the structural, mechanical, and chemical properties of cementitious matrices through interfacial interactions. Quantitative analyses indicated that the optimized incorporation of nanomaterials could enhance the 28-day compressive strength by up to 80%, the flexural strength by 150%, and the fracture energy by over 130% compared to the control mixes. MD simulations demonstrated that functionalized CNTs formed robust hydrogen bonds with calcium silicate hydrate (C–S–H), whereas GO enhanced interfacial adhesion through Ca–O ionic bridges and GO edge functionalization, aligning closely with experimental trends. Through multiscale integration, ML models trained on high-fidelity DFT data can enable predictive mapping from atomistic behavior to macroscopic properties, reducing the computational cost by over 90% while maintaining quantum-level accuracy. This review also identifies key limitations of current modeling tools, including force field fidelity, scale-bridging constraints, and data scarcity, and proposes scientifically grounded remediation strategies using transfer learning, PINNs, and reinforcement learning to enhance accuracy and generalization. This review also outlines the sustainability, economics, and translational pathways for advancing nanomaterial-enhanced cementitious systems from design to infrastructure application. This study provides a blueprint for the rational design of next-generation nanomaterial-enhanced concrete, offering both theoretical insights and practical directions for future research by bridging nanoscale mechanisms with macroscale performance.