  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.
