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The generation of multiphase porous electrode microstructures is a criticalstep in the optimisation of electrochemical energy storage devices. This workimplements a deep convolutional generative adversarial network (DC-GAN) forgenerating realistic n-phase microstructural data. The same networkarchitecture is successfully applied to two very different three-phasemicrostructures: A lithium-ion battery cathode and a solid oxide fuel cellanode. A comparison between the real and synthetic data is performed in termsof the morphological properties (volume fraction, specific surface area,triple-phase boundary) and transport properties (relative diffusivity), as wellas the two-point correlation function. The results show excellent agreementbetween for datasets and they are also visually indistinguishable. By modifyingthe input to the generator, we show that it is possible to generatemicrostructure with periodic boundaries in all three directions. This has thepotential to significantly reduce the simulated volume required to beconsidered representative and therefore massively reduce the computational costof the electrochemical simulations necessary to predict the performance of aparticular microstructure during optimisation.

Pores for thought: The use of generative adversarial networks for the  stochastic reconstruction of 3D multi-phase electrode microstructures with  periodic boundaries