Generative Design of Material Microstructures for Organic Solar Cells using Diffusion Models

Score-based methods, particularly denoising diffusion probabilistic models (DDPMs), have demonstrated impressive improvements to the state-of-the-art (SOTA) in generative modeling. DDPM models and related variants, all broadly categorized under diffusion models, are not only applicable to generating entertaining art but are appealing to a wider variety of applications. In this work, we compare the performance of a diffusion model with a Wasserstein Generative Adversarial Network in generating two-phase microstructures of photovoltaic cells. We demonstrate the diffusion model's performance improvements at generating realistic-looking microstructures, as well as its ability to cover several modes in the target distribution.