| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
| Presentation Title |
Inverse Alloy Design: An Alloy Composition Generation Framework With Flexibilities |
| Author(s) |
Mohammad Abu-Mualla, Ellis R. Crabtree, Fredrick N. Michael, Yayue Pan, Jida Huang |
| On-Site Speaker (Planned) |
Jida Huang |
| Abstract Scope |
Inverse design, generating material compositions with targeted properties, offers a promising approach to material discovery. Machine learning has emerged as a key enabler for generative design. However, existing models face challenges such as non-uniqueness (multiple compositions yield the same property), the complexity of high-dimensional compositional spaces, and the generation of physically non-achievable alloys. This work proposes a latent diffusion generative model for the inverse problem and a Variational Autoencoder (VAE) to obtain the latent representation; we constrained the latent space to ensure the generation of physically valid alloys. Our framework successfully mapped both the composition-property and property-composition correlations, using the VAE for the forward and the latent diffusion model for the inverse design. The model is trained on the Granta Alloys dataset. Experimental results reveal the proposed framework could accurately predict both pathways and generate multiple feasible designs, providing flexibility for selecting compositions that meet target properties. |
| Proceedings Inclusion? |
Undecided |