https://github.com/aspirincode/awesome_for_material_discovery_using_dl

https://github.com/aspirincode/awesome_for_material_discovery_using_dl

List: awesome_for_material_discovery_using_dl

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/aspirincode/awesome_for_material_discovery_using_dl
• Owner: AspirinCode
• License: gpl-3.0
• Created: 2023-05-04T11:37:13.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-05-06T10:24:46.000Z (almost 3 years ago)
• Last Synced: 2024-05-21T08:06:12.126Z (almost 2 years ago)
• Size: 16.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# awesome_for_material_discovery_using_DL

![contributing-image](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)

related to  **Generative AI** and **Deep Learning** for  **Material_Discovery**.

## Deep Learning-Material_Discovery

### VAE-based

* **Crystal Diffusion Variational Autoencoder for Periodic Material Generation** [2022]  

  Xie, Tian, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola.  

  [Paper](http://people.csail.mit.edu/tommi/papers/Xie_etal_ICLR2022.pdf) |  [code](https://github.com/txie-93/cdvae) 

### GAN-based

* **Generative adversarial networks and diffusion models in material discovery** [2023]  

Alverson, Michael, Sterling Baird, Ryan Murdock, and Taylor Sparks.   

[Paper](https://doi.org/10.26434/chemrxiv-2022-6l4pm-v2) 

### Diffusion-based

* **Generative adversarial networks and diffusion models in material discovery** [2023]  

Alverson, Michael, Sterling Baird, Ryan Murdock, and Taylor Sparks.   

[Paper](https://doi.org/10.26434/chemrxiv-2022-6l4pm-v2)