  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.
