# MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN

arXiv: https://arxiv.org/abs/2210.06541 · HTML: https://mlsanity.com/paper/2210.06541/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Generating 3D structures from a 2D slice with GAN-based dimensionality expansion](https://mlsanity.com/paper/2102.07708/) (2021)

## Built on by (most cited first, 5 of 5)

- [Materials science in the era of large language models: a perspective](https://mlsanity.com/paper/2403.06949/) (2024)
- [Topology Estimation of Simulated 4D Image Data by Combining Downscaling and Convolutional Neural Networks](https://mlsanity.com/paper/2306.14442/) (2023)
- [Prediction of microstructural representativity from a single image](https://mlsanity.com/paper/2410.19568/) (2024)
- [MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation](https://mlsanity.com/paper/2508.20138/) (2025)
- [Physics-Guided Generative AI for Property-Targeted 3D Porous Media Design](https://mlsanity.com/paper/2607.24274/) (2026)

## Abstract (arXiv)

3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex material systems behave under applied stresses, temperatures and chemical conditions. However, 3D imaging of materials is challenging for a number of reasons, including limited field of view, low resolution and difficult sample preparation. Recently, a machine learning method, SliceGAN, was developed to statistically generate 3D microstructural datasets of arbitrary size using a single 2D input slice as training data. In this paper, we present the results from applying SliceGAN to 87 different microstructures, ranging from biological materials to high-strength steels. To demonstrate the accuracy of the synthetic volumes created by SliceGAN, we compare three microstructural properties between the 2D training data and 3D generations, which show good agreement. This new microstructure library both provides valuable 3D microstructures that can be used in models, and also demonstrates the broad applicability of the SliceGAN algorithm.