  Generative adversarial networks (GANs) can be trained to generate 3D image
data, which is useful for design optimisation. However, this conventionally
requires 3D training data, which is challenging to obtain. 2D imaging
techniques tend to be faster, higher resolution, better at phase identification
and more widely available. Here, we introduce a generative adversarial network
architecture, SliceGAN, which is able to synthesise high fidelity 3D datasets
using a single representative 2D image. This is especially relevant for the
task of material microstructure generation, as a cross-sectional micrograph can
contain sufficient information to statistically reconstruct 3D samples. Our
architecture implements the concept of uniform information density, which both
ensures that generated volumes are equally high quality at all points in space,
and that arbitrarily large volumes can be generated. SliceGAN has been
successfully trained on a diverse set of materials, demonstrating the
widespread applicability of this tool. The quality of generated micrographs is
shown through a statistical comparison of synthetic and real datasets of a
battery electrode in terms of key microstructural metrics. Finally, we find
that the generation time for a $10^8$ voxel volume is on the order of a few
seconds, yielding a path for future studies into high-throughput
microstructural optimisation.
