标题： 基于生成对抗网络的闭环电极微结构设计的深度核贝叶斯优化方法 显示英文标题

标题： Deep Kernel Bayesian Optimisation for Closed-Loop Electrode Microstructure Design with User-Defined Properties based on GANs

摘要： 生成具有最佳形态和传输特性的多相多孔电极微观结构对于改进电化学储能设备的设计至关重要，例如锂离子电池。 电极特性通过作为电化学反应与传输过程发生的主要场所而直接影响电池性能。 本工作提出了一种生成-优化闭环算法，用于设计具有定制特性的微观结构。 使用深度卷积生成对抗网络作为深度内核，并用于生成多孔锂离子电池阴极材料的合成三相三维图像。 高斯过程回归利用生成器的潜在空间，并作为代理模型来关联合成微观结构的形态和传输特性。 该代理模型被集成到深度内核贝叶斯优化框架中，该框架根据生成器的潜在空间优化阴极特性。 定义了一组目标函数以实现形态特性（例如，体积分数、比表面积）和传输特性（相对扩散率）的最大化。 我们展示了同时最大化相关特性（比表面积和相对扩散率）以及这些特性的约束优化的能力。 这是在感兴趣相的体积分数保持恒定值的情况下对形态或传输特性进行最大化。 可视化优化后的潜在空间揭示了其与形态特性之间的相关性，从而能够快速生成具有定制特性的视觉上逼真的微观结构。 显示英文摘要

摘要： The generation of multiphase porous electrode microstructures with optimum morphological and transport properties is essential in the design of improved electrochemical energy storage devices, such as lithium-ion batteries. Electrode characteristics directly influence battery performance by acting as the main sites where the electrochemical reactions coupled with transport processes occur. This work presents a generation-optimisation closed-loop algorithm for the design of microstructures with tailored properties. A deep convolutional Generative Adversarial Network is used as a deep kernel and employed to generate synthetic three-phase three-dimensional images of a porous lithium-ion battery cathode material. A Gaussian Process Regression uses the latent space of the generator and serves as a surrogate model to correlate the morphological and transport properties of the synthetic microstructures. This surrogate model is integrated into a deep kernel Bayesian optimisation framework, which optimises cathode properties as a function of the latent space of the generator. A set of objective functions were defined to perform the maximisation of morphological properties (e.g., volume fraction, specific surface area) and transport properties (relative diffusivity). We demonstrate the ability to perform simultaneous maximisation of correlated properties (specific surface area and relative diffusivity), as well as constrained optimisation of these properties. This is the maximisation of morphological or transport properties constrained by constant values of the volume fraction of the phase of interest. Visualising the optimised latent space reveals its correlation with morphological properties, enabling the fast generation of visually realistic microstructures with customised properties.