用大模型辅助生成合金，加速新材料发现。
Inverse Materials Design by Large Language Model-Assisted Generative Framework
- 结合大模型文本挖掘与生成对抗网络，自动筛选材料候选。
- 预测金属玻璃热力学性能误差小于8%，接近实验结果。
- 适合材料科学领域研究者快速探索新合金设计。
深度生成模型在逆向材料设计中前景广阔，但受限于数据稀缺和模型架构。本文提出AlloyGAN，一种闭环框架，融合大语言模型（LLM）辅助文本挖掘与条件生成对抗网络（CGAN），提升数据多样性并优化逆向设计。以合金发现为例，AlloyGAN通过迭代筛选与实验验证系统性优化材料候选。对于金属玻璃，该框架预测的热力学性质与实验值偏差低于8%，展现强鲁棒性。通过连接生成式AI、领域知识与验证流程，AlloyGAN提供可扩展的方法，加速具有特定性能的新材料发现，为材料科学应用开辟新路径。
原文摘要 · Abstract (English)
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.
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