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

Abstract

Score-based methods, particularly denoising diffusion probabilistic models (DDPMs), have demonstrated impressive improvements to state-of-the-art generative modeling. Due to their impressive ability to sample from complex distributions, DDPM models and related variants, all broadly categorized under diffusion models, apply to various 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 in generating realistic-looking microstructures and its ability to cover several modes of the target distribution.

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Cite

Text

Herron et al. "Generative Design of Material Microstructures for Organic Solar Cells Using Diffusion Models." NeurIPS 2022 Workshops: AI4Mat, 2022.

Markdown

[Herron et al. "Generative Design of Material Microstructures for Organic Solar Cells Using Diffusion Models." NeurIPS 2022 Workshops: AI4Mat, 2022.](https://mlanthology.org/neuripsw/2022/herron2022neuripsw-generative/)

BibTeX

@inproceedings{herron2022neuripsw-generative,
  title     = {{Generative Design of Material Microstructures for Organic Solar Cells Using Diffusion Models}},
  author    = {Herron, Ethan and Lee, Xian Yeow and Balu, Aditya and Pokuri, Balaji Sesha Sarath and Ganapathysubramanian, Baskar and Sarkar, Soumik and Krishnamurthy, Adarsh},
  booktitle = {NeurIPS 2022 Workshops: AI4Mat},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/herron2022neuripsw-generative/}
}