Improving Direct Physical Properties Prediction of Heterogeneous Materials from Imaging Data via Convolutional Neural Network and a Morphology-Aware Generative Model

TL;DR

This paper introduces a morphology-constrained generative model that produces artificial microstructure samples to enhance material property prediction, reducing data requirements and improving accuracy in heterogeneous material analysis.

Contribution

It presents a novel morphology-aware generative model that generates realistic microstructures from limited data, improving property prediction over existing models.

Findings

Artificial samples better match authentic material property distributions.

The model outperforms Markov Random Field in generating realistic microstructures.

Enhanced prediction accuracy demonstrated with the proposed approach.

Abstract

Direct prediction of material properties from microstructures through statistical models has shown to be a potential approach to accelerating computational material design with large design spaces. However, statistical modeling of highly nonlinear mappings defined on high-dimensional microstructure spaces is known to be data-demanding. Thus, the added value of such predictive models diminishes in common cases where material samples (in forms of 2D or 3D microstructures) become costly to acquire either experimentally or computationally. To this end, we propose a generative machine learning model that creates an arbitrary amount of artificial material samples with negligible computation cost, when trained on only a limited amount of authentic samples. The key contribution of this work is the introduction of a morphology constraint to the training of the generative model, that enforces the resultant artificial material samples to have the same morphology distribution as the authentic ones. We show empirically that the proposed model creates artificial samples that better match with the authentic ones in material property distributions than those generated from a state-of-the-art Markov Random Field model, and thus is more effective at improving the prediction performance of a predictive structure-property model.