Conditional diffusion-based microstructure reconstruction

TL;DR

This paper explores the use of diffusion models for reconstructing complex microstructures in materials science, demonstrating their effectiveness over traditional GAN-based methods and validating their applicability with diverse datasets.

Contribution

It introduces diffusion models for microstructure reconstruction, showing their ability to generate realistic, diverse micrographs from limited data, surpassing GAN-based approaches.

Findings

Diffusion models produce microstructures visually indistinguishable from real data.

The approach works well with small datasets typical of laboratory conditions.

Quantitative metrics confirm high quality and diversity of reconstructions.

Abstract

Microstructure reconstruction, a major component of inverse computational materials engineering, is currently advancing at an unprecedented rate. While various training-based and training-free approaches are developed, the majority of contributions are based on generative adversarial networks. In contrast, diffusion models constitute a more stable alternative, which have recently become the new state of the art and currently attract much attention. The present work investigates the applicability of diffusion models to the reconstruction of real-world microstructure data. For this purpose, a highly diverse and morphologically complex data set is created by combining and processing databases from the literature, where the reconstruction of realistic micrographs for a given material class demonstrates the ability of the model to capture these features. Furthermore, a fiber composite data set is used to validate the applicability of diffusion models to small data set sizes that can realistically be created by a single lab. The quality and diversity of the reconstructed microstructures is quantified by means of descriptor-based error metrics as well as the Fr\'echet inception distance (FID) score. Although not present in the training data set, the generated samples are visually indistinguishable from real data to the untrained eye and various error metrics are computed. This demonstrates the utility of diffusion models in microstructure reconstruction and provides a basis for further extensions such as 2D-to-3D reconstruction or application to multiscale modeling and structure-property linkages.