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Silica aerogels are highly porous ultralight materials with extremely low density and thermal conductivity. These exceptional properties of silica aerogels are often accounted to microstructure morphology, thus making them of keen research interest for analysing their structure‐property relationships. The classical approach for this involved the microstructure modelling of the silica aerogels with aggregation‐based modelling algorithm viz., diffusion‐limited cluster‐cluster aggregation (DLCA) and then performing finite element method (FEM) on the generated representative volume element (RVEs). However, the process often requires large computation time and resources.

The objective of this work was thus to introduce an artificial intelligence approach based on neural networks and reinforcement learning to eliminate the necessity of generating and simulating 3D silica aerogel models for predicting their structural …