Conditional generative AI for high-fidelity synthesis of hydrating cementitious microstructures

• A conditional generative AI model synthesizes high-fidelity 3D cement microstructures with varying w/c ratios, Blaine values, and curing ages. • A two-stage latent representation approach with VQVAE efficiently compresses 3D datasets while preserving key microstructural details. • A diffusion transformer with slice-depth blocks enables realistic microstructure synthesis by capturing 3D distributions. • Generated microstructures closely match real ones in statistical distributions, phase evolution, and particle size characteristics. Portland cement paste has a highly heterogenous evolving microstructure that complicates the development of stronger and greener cementitious materials. Microstructure is the fundamental input of multiscale studies on material behaviors. Herein, we propose a conditional generative AI framework for synthesizing high-fidelity 3D microstructures of hydrating cement paste (1–28 days) with varying water-to-cement ratios and Blaine fineness values. A latent diffusion transformer, operating within a compact two-stage latent space derived via a vector quantized variational autoencoder, efficiently captures and reproduces experimentally measured microstructural patterns. Statistical analyses confirm strong consistency in grey value distributions, micromechanical properties, hydration phase evolution, and particle size distributions, with only minor boundary-related discrepancies. Validation using a pretrained classifier further corroborates the fidelity of generated microstructures. This approach provides a robust tool for realistic cement paste microstructure generation, supporting multiscale modeling and advancing the design of sustainable cementitious materials.