About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Materials
|
Presentation Title |
M-17: Generative Adversarial Networks and Diffusion Models in Material Discovery |
Author(s) |
Michael Alverson, Sterling Baird, Taylor Sparks |
On-Site Speaker (Planned) |
Michael Alverson |
Abstract Scope |
The idea of material discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced machine learning methods for predicting entirely new crystal structures. In this work, we had three primary objectives. I) Introduce CrysGraph, a crystal encoding that can be used in a wide variety of deep learning generative models. II) Investigate and analyze the performance of Generative Adversarial Networks (GANs) and Diffusion models to find an innovative and effective way of generating theoretical crystal structures that are synthesizable and stable. III) Show that the models that have a better "understanding" of the structure of CrysGraph produce more symmetrical and realistic crystals and exhibit a better apprehension of the dataset as a whole. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, |