Deep generative models hold great promise for inverse materials design, yet their efficiency and accuracy remain constrained by data scarcity and model architecture. Here, we introduce AlloyGAN, a closed-loop framework that integrates Large Language Model (LLM)-assisted text mining with Conditional Generative Adversarial Networks (CGANs) to enhance data diversity and improve inverse design. Taking alloy discovery as a case study, AlloyGAN systematically refines material candidates through iterative screening and experimental validation. For metallic glasses, the framework predicts thermodynamic properties with discrepancies of less than 8% from experiments, demonstrating its robustness. By bridging generative AI with domain knowledge and validation workflows, AlloyGAN offers a scalable approach to accelerate the discovery of materials with tailored properties, paving the way for broader applications in materials science.
@article{arxiv.2502.18127,
title = {Inverse Materials Design by Large Language Model-Assisted Generative Framework},
author = {Yun Hao and Che Fan and Beilin Ye and Wenhao Lu and Zhen Lu and Peilin Zhao and Zhifeng Gao and Qingyao Wu and Yanhui Liu and Tongqi Wen},
journal= {arXiv preprint arXiv:2502.18127},
year = {2025}
}