Data-driven inverse design and optimisation of silica aerogel model networks

Kurzfassung

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 and mechanical properties. To this end for the forward prediction of the elastic modulus and fractal dimension of the silica aerogels from DLCA parameters, an artificial neural network was developed. Furthermore, to reverse engineer the material and perform inverse material design, a reinforcement learning framework was developed, that is shown to have learned to determine appropriate DLCA model parameters as actions for a desired fractal dimension and elastic modulus.