  Using a large-scale, experimentally captured 3D microstructure dataset, we
implement the generative adversarial network (GAN) framework to learn and
generate 3D microstructures of solid oxide fuel cell electrodes. The generated
microstructures are visually, statistically, and topologically realistic, with
distributions of microstructural parameters, including volume fraction,
particle size, surface area, tortuosity, and triple phase boundary density,
being highly similar to those of the original microstructure. These results are
compared and contrasted with those from an established, grain-based generation
algorithm (DREAM.3D). Importantly, simulations of electrochemical performance,
using a locally resolved finite element model, demonstrate that the GAN
generated microstructures closely match the performance distribution of the
original, while DREAM.3D leads to significant differences. The ability of the
generative machine learning model to recreate microstructures with high
fidelity suggests that the essence of complex microstructures may be captured
and represented in a compact and manipulatable form.
