Abstract
The generation of multiphase porous electrode microstructures is a critical step in the optimisation of electrochemical energy storage devices. This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data. The same network architecture is successfully applied to two very different three-phase microstructures: A lithium-ion battery cathode and a solid oxide fuel cell anode. A comparison between the real and synthetic data is performed in terms of the morphological properties (volume fraction, specific surface area, triple-phase boundary) and transport properties (relative diffusivity), as well as the two-point correlation function. The results show excellent agreement between datasets and they are also visually indistinguishable. By modifying the input to the generator, we show that it is possible to generate microstructure with periodic boundaries in all three directions. This has the potential to significantly reduce the simulated volume required to be considered “representative” and therefore massively reduce the computational cost of the electrochemical simulations necessary to predict the performance of a particular microstructure during optimisation.
Highlights
The geometrical properties of multiphase materials are of central importance to a wide variety of engineering disciplines
This work presents a method for generating synthetic threedimensional microstructures composed of any number of distinct material phases, through the implementation of deep convolutional generative adversarial network (DC-Generative Adversarial Networks (GANs))
The results showed excellent agreement across all metrics, the synthetic structures showed a smaller variance compared to the training data, which is a npj Computational Materials (2020) 82
Summary
The geometrical properties of multiphase materials are of central importance to a wide variety of engineering disciplines. The microstructure of these electrodes significantly impacts their performance and their morphological optimisation is vital for developing the generation of energy storage technologies[6]. Recent improvements in 3D imaging techniques such as X-ray computed tomography (XCT) have allowed researchers to view the microstructure of porous materials at sufficient resolution to extract relevant metrics[7,8,9,10]. A variety of challenges remain, including how to extract the key metrics or “essence“ of an observed microstructural dataset such that synthetic volumes with equivalent properties can be generated, and how to modify specific attributes of this microstructural data without compromising its overall resemblance to the real material
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