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

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 nphase 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.

3D Quantification of Microstructural Properties of LiNi0.5Mn0.3Co0.2O2 High‐Energy Density Electrodes by X‐Ray Holographic Nano‐Tomography

“…Recently, Gayon‐Lombardo et al. [ 78 ] proposed to use periodic boundaries instead of Dirichlet boundaries for the diffusion‐based method for the conventional tortuosity factor determination. They suggested that this approach allowed representing better the bulk behavior of the microstructure.…”

How Machine Learning Will Revolutionize Electrochemical Sciences

“…In addition, following segmentation, Generative Adversarial Networks (GANs) are now being developed to learn the phase arrangement in segmented data and generate mesostructure realizations with customized properties in volumes larger than could be obtained from imaging alone ( Figure 3 e,f). 43 …”

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

“…[168][169][170][171][172][173][174][175] In conjunction with the experimental techniques and mathematical models introduced in Section 3, AI techniques have been successfully employed in the study of energy materials. Various machine learning (ML) methods and advanced deep neural networks (DNN) have shown excellent performance in regard to material structure reconstruction and generation, and property and performance prediction, such as artificial neural networks (ANN), 174 support vector machines (SVM), 176 convolutional neural networks (CNN), [177][178][179][180][181][182] generative adversarial neural networks (GANN) [183][184][185][186][187][188][189][190] and so on. Fig.…”