3D Microstructure Reconstruction of Aerogels via Conditional GANs

Prakul Pandit, Sugan Kanagasenthinathan, Ameya Rege

Submission Track: Paper Track (Tiny Paper)

Submission Category: AI-Guided Design

Keywords: conditional GAN, microstructure reconstruction, aerogels, rapid materials development, AI for science

TL;DR: Reconstrucing aerogels microstructure for rapid pore space characterisation through synthetic SEM images with conditional GANs

Abstract:

Aerogels are low-density and highly porous materials (90–99% porosity) with
exceptional thermal and mechanical properties, governed by their intricate
nanoporous microstructure. Understanding their structure-property relationships
is essential for optimizing their performance across industrial applications. A sig-
nificant challenge appears in precisely identifying the complete pore space and
thus mapping their microstructural morphology of aerogels. This work presents a
deep learning-driven digital twin framework for aerogels, leveraging Conditional
Generative Adversarial Networks (cGANs) and Convolutional Neural Networks
(CNNs) for 3D microstructure reconstruction and predictive modeling. Our ap-
proach reconstructs 3D aerogel microstructures from synthetic 2D scanning elec-
tron microscopy (SEM) images that mimic real samples by incorporating depth
effects. A CNN predicts key microstructural parameters, including pore radius,
relative density, and pore size distribution, with minimal error. A 3D cGAN then
generates aerogel microstructures by capturing global spatial features and condi-
tioning on the extracted parameters.
We demonstrate that conditioning improves the fidelity of reconstruction by en-
forcing physically meaningful constraints. This method provides a scalable, data-
driven approach for microstructure modeling, enabling efficient structure-property
predictions, and guiding aerogel design for targeted applications.

Submission Number: 45