Conditional diffusion-based microstructure reconstruction

Research output: Contribution to journalResearch articleContributedpeer-review



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 Fréchet 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.


Original languageEnglish
Article number105608
JournalMaterials today communications
Early online date16 Feb 2023
Publication statusPublished - Jun 2023

External IDs

Scopus 85150431350
Mendeley d6a9b2da-a3fa-369b-b907-66220116ec48
ORCID /0000-0003-3358-1545/work/142237193
ORCID /0000-0003-1370-064X/work/142243841
WOS 001001716200001



  • Microstructure, Reconstruction, Diffusion models, Machine learning