Locating, characterizing, and understanding the microstructural nanomodifications of random heterogeneous materials are critical to improve their performance, but current methods are limited by the material’s highly disordered microstructure. Here, we proposed a scheme to reveal the hidden microstructural modifications by coupling large-scale nanoporosity mappings with deep learning and demonstrated its effectiveness on nanomaterial (graphene oxide, GO)-modified cement. This deep learning-based approach showed superior abilities in distinguishing the nanomodified samples. The deep learning-approach shows a classification accuracy of 88.8% using nanoporosity mapping with micro-scale features. When nano-scale features were included, the classification accuracy further increased to 98%, indicating the GO modifications in the nano-scale. The microstructural changes in nano and micro scales were also located, providing the first direct evidence of a localized modification effect of GO where a small proportion (3.6–5.4%) of characteristic regions contribute up to 70% of total relevance. The effect of GO on spatial heterogeneity was also revealed by the changes in characteristic lengths. Besides, the reduced fraction of shared pore structural patterns from 44.6 to 40.9% under a high dosage of GO (0.02 wt %) also indicated the nanomodification effect of GO in improving structural topological efficiency. This study not only provides insights into the GO-modified cement for structural applications but also promotes the development of other random heterogeneous materials for a wide range of applications such as load-bearing, thermal and electrical insulation, catalyst support, and so on.

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