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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="ZeoDIFF GITHUB PAGE TEST">
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<title>Inverse design of porous materials: a diffusion model approach</title>
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<h1 class="title is-1 publication-title">Inverse design of porous materials: A diffusion model approach</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?user=rVGLAYIAAAAJ&hl=en&oi=ao">Junkil Park</a><sup>1</sup>,</span>
<span class="author-block">
<a href=>Aseem Partap Singh Gill</a><sup>2</sup>,</span>
<span class="author-block">
<a href=>Seyed Mohamad Moosavi</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=OUoa6QgAAAAJ&hl=en&oi=ao">Jihan Kim</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Korea Advanced Institue of Science and Technology,</span>
<span class="author-block"><sup>2</sup>University of Toronto</span>
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<h2 class="title is-3">Abstract</h2>
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<p>
The success of diffusion models in the field of image processing has propelled the creation of software such as Dall-E,
Midjourney and Stable Diffusion, which are tools used for text-to-image generation. Mapping this workflow onto material
discovery, a new diffusion model was developed for the generation of pure silica zeolite, marking one of the first applications of
diffusion models to porous materials. Our model demonstrates the ability to generate novel crystalline porous materials that
are not present in the training dataset, while exhibiting exceptional performance in inverse design tasks targeted on various
chemical properties including the void fraction, Henry coefficient and heat of adsorption. Comparing our model with a
Generative Adversarial Network (GAN) revealed that the diffusion model outperforms the GAN in terms of structure validity,
exhibiting an over 2000-fold improvement in performance. We firmly believe that diffusion models (along with other deep
generative models) hold immense potential in revolutionizing the design of new materials, and anticipate the wide extension of
our model to other classes of porous materials.
</p>
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<h2 class="subtitle has-text-centered">
<span class="dnerf">ZeoDiff Generation Process
</h2>
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</section>