# Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material Design

arXiv: https://arxiv.org/abs/1612.07401 · HTML: https://mlsanity.com/paper/1612.07401/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2016.

## Built on by (most cited first, 12 of 12)

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- [Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review](https://mlsanity.com/paper/2303.07647/) (2023)
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- [DA-VEGAN: Differentiably Augmenting VAE-GAN for microstructure reconstruction from extremely small data sets](https://mlsanity.com/paper/2303.03403/) (2023)
- [A Comprehensive Survey on Machine Learning Driven Material Defect Detection](https://mlsanity.com/paper/2406.07880/) (2024)
- [Self-supervised optimization of random material microstructures in the small-data regime](https://mlsanity.com/paper/2108.02606/) (2021)
- [Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders](https://mlsanity.com/paper/2107.10661/) (2021)
- [An Uncertainty-aware Deep Learning Framework-based Robust Design Optimization of Metamaterial Units](https://mlsanity.com/paper/2407.20251/) (2024)
- [Data-Driven Learning of 3-Point Correlation Functions as Microstructure Representations](https://mlsanity.com/paper/2109.02255/) (2021)
- [A Meta-Generation framework for Industrial System Generation](https://mlsanity.com/paper/2306.05123/) (2023)
- [Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs](https://mlsanity.com/paper/2312.07832/) (2023)

## Abstract (arXiv)

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.