# A Conditional Generative Model for Predicting Material Microstructures from Processing Methods

arXiv: https://arxiv.org/abs/1910.02133 · HTML: https://mlsanity.com/paper/1910.02133/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2019.

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

- [Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions](https://mlsanity.com/paper/2211.08179/) (2022)
- [Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare](https://mlsanity.com/paper/2410.07525/) (2024)
- [GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects](https://mlsanity.com/paper/2503.04776/) (2025)
- [Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Towards ML-assisted Advanced Manufacturing](https://mlsanity.com/paper/2302.08495/) (2023)
- [Parameter-aware high-fidelity microstructure generation using stable diffusion](https://mlsanity.com/paper/2507.00459/) (2025)

## Abstract (arXiv)

Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications. Thus a critical task in material design is establishing the processing-structure relationship, which requires domain expertise and techniques that can model the high-dimensional material microstructure. This work proposes a deep learning based approach that models the processing-structure relationship as a conditional image synthesis problem. In particular, we develop an auxiliary classifier Wasserstein GAN with gradient penalty (ACWGAN-GP) to synthesize microstructures under a given processing condition. This approach is free of feature engineering, requires modest domain knowledge and is applicable to a wide range of material systems. We demonstrate this approach using the ultra high carbon steel (UHCS) database, where each microstructure is annotated with a label describing the cooling method it was subjected to. Our results show that ACWGAN-GP can synthesize high-quality multiphase microstructures for a given cooling method.