Authors: Yosuke Komatsu | Anna Ściążko | Naoki Shikazono
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Application of Denoising Diffusion Probabilistic Model for Synthesizing Composite Electrode Microstructures for Solid Oxide Cells The microstructural design of composite electrodes plays a pivotal role in determining the performance and durability of solid oxide cells (SOCs). The transport phenomena of ion and electron, as well as gas diffusion within the electrode are affected by their complex porous microstructures. Furthermore, they facilitate the electrochemically active reaction sites among the transport pathways. Therefore, optimizations of the electrode microstructures are of great importance. Traditional methods for synthesizing and optimizing electrode microstructures often involve extensive trial and error, limiting their efficiency and scalability. Experimental observation, reconstruction and quantification of the microstructures are also challenging because of the complex nature of porous electrodes. Synthesizing electrode microstructures through numerical, statistical or machine learning approaches is beneficial, because it has the potential to reduce time and cost in the electrode development process. Recently, generative adversarial networks (GANs) have attracted much attention in the synthesis of electrode microstructures. GAN consists of two competing neural networks—a generator and a discriminator—that iteratively improve each other to create highly realistic outputs. Sciazko et al. [1], Yamagishi et al. [2], and Kishimoto et al. [3] have demonstrated the utility of GANs in synthesizing porous composite electrode microstructures and optimizing their properties. Despite their popularity, GAN often suffers from mode collapse where the generator produces limited variations of microstructures, which reduces their overall applicability. Additionally, GAN requires careful hyperparameter tuning and may struggle to accurately generate complex microstructural details. Denoising diffusion probabilistic models (DDPMs) which is a class of generative machine learning models have demonstrated exceptional capability in modeling complex and high-dimensional data distributions [4]. DDPMs effectively regenerate high-quality likelihood samples from random noise by reversely denoising the learned sequential noise-adding forward process. DDPMs can offer several advantages over GANs for microstructure synthesis. One crucial benefit is their inherent stability during training, as they do not rely on adversarial dynamics between competing networks. This stability allows DDPMs to capture a more comprehensive distributions of microstructural patterns, resulting in diverse and realistic outputs. The denoising process in DDPMs provides a robust mechanism for controlling the generative process. Ho et al. [4] has established the foundational principles of DDPMs, highlighting their superior generative capabilities in image synthesis tasks. DDPM gains attentions in microstructure generations for porous materials. Lyu and Ren [5] reported that conditional DDPM can be used to control the physical properties, especially permeability, of the generated three dimensional (3D) single solid phase porous material. Bentamou et al. [6] adopted one-hot encoding process to a DDPM and synthesised a 3D microstructure multiphase porous structure using Lanthanum strontium cobalt ferrite - gadolinium-doped ceria composite electrode, in which spatial complexities of the composite electrode were captured. However, compared to GAN, there are still some issues with the accuracy of the morphology of the synthesized grains. This study explores the application of DDPMs for the synthesis and generation of SOC composite electrode microstructures and further attempts controlling the physical properties of the electrode structures. The DDPM framework adopts a U-Net-based network to learn nosing characteristics in the forward process of the training data (forward process in Fig. 1a), and later the trained network was used for synthesizing the microstructure by denoising from the random noise (backward process in Fig. 1a). As an example, a microstructure of nickel – gadolinium doped ceria (Ni-GDC) electrode is generated by DDPM. Grayscale sequential images acquired from FIB-SEM reconstructions are segmented and then used in the DDPM for training. Then, the Ni-GDC microstructure is synthesized. The trained network can produce a likelihood structure to the Ni-GDC electrode as shown in Fig. 1b. To quantify the accuracy of DDPM synthesis, the statistical parameters of both real and synthesized microstructures are evaluated. Figure 1c shows the volume fractions of Ni, GDC and pore, and total and active triple phase boundary (TPB) densities. The results reveal good agreement between the DDPM-generated microstructure and the real microstructure, where the deviation in the volume fraction is less than 10%. Despite its promising potential, there are challenges to be addressed for the improvement of DDPM. These include the need for large and diverse training datasets, optimizing adequate network architecture, as well as computational demands associated with model training and inference. Acknowledgments This work was partly supported by the New Energy and Industrial Technology Development Organization (NEDO) and by Advanced Research Infrastructure for Materials and Nanotechnology in Japan (ARIM Japan). References: Sciazko et al., ECS Transactions , 103(1), 1363, (2021). Yamagishi et al., ECS Meeting Abstracts , MA2022-01, 1683, (2022). Kishimoto et al., Journal of Power Sources , 580, 233411, (2023) Ho, J., Jain, A., &amp; Abbeel, P. arXiv:2006.11239, (2020). Lyu et al., Scientific reports , 14, 5041, (2024). Bentamou et al., Computational Materials Science , 248, 113596, (2025). Figure Captions: Figure 1. A) Scheme of forward and backward processes for SOC electrode microstructure synthesis using denoising diffusion probabilistic model. B) Comparison of real and synthesized microstructures of nickel-gadolinium doped ceria (Ni-GDC) electrode, C) the volume fractions and total and active TPB densities of real and synthesized microstructures. Figure 1 Electrode ; Microstructure ; Probabilistic logic ; Diffusion ; Composite number ; Generator (circuit theory) ; Porosity ; Oxide Machine Learning in Materials Science ; Domain Adaptation and Few-Shot Learning ; Ferroelectric and Negative Capacitance Devices