# A Transfer Learning Approach for Microstructure Reconstruction and Structure-property Predictions

arXiv: https://arxiv.org/abs/1805.02784 · HTML: https://mlsanity.com/paper/1805.02784/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2018.

## Built on by (most cited first, 21 of 21)

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- [Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs](https://mlsanity.com/paper/2312.07832/) (2023)
- [3D variational autoencoder for fingerprinting microstructure volume elements](https://mlsanity.com/paper/2503.17427/) (2025)
- [Parameter-aware high-fidelity microstructure generation using stable diffusion](https://mlsanity.com/paper/2507.00459/) (2025)

## Abstract (arXiv)

Stochastic microstructure reconstruction has become an indispensable part of computational materials science, but ongoing developments are specific to particular material systems. In this paper, we address this generality problem by presenting a transfer learning-based approach for microstructure reconstruction and structure-property predictions that is applicable to a wide range of material systems. The proposed approach incorporates an encoder-decoder process and feature-matching optimization using a deep convolutional network. For microstructure reconstruction, model pruning is implemented in order to study the correlation between the microstructural features and hierarchical layers within the deep convolutional network. Knowledge obtained in model pruning is then leveraged in the development of a structure-property predictive model to determine the network architecture and initialization conditions. The generality of the approach is demonstrated numerically for a wide range of material microstructures with geometrical characteristics of varying complexity. Unlike previous approaches that only apply to specific material systems or require a significant amount of prior knowledge in model selection and hyper-parameter tuning, the present approach provides an off-the-shelf solution to handle complex microstructures, and has the potential of expediting the discovery of new materials.