Inverse Materials Design by Large Language Model-Assisted Generative Framework
This work addresses the challenge of accelerating materials discovery for materials scientists, though it is incremental as it builds on existing generative models by integrating LLMs and validation workflows.
The paper tackles the problem of inefficient and inaccurate inverse materials design due to data scarcity and model limitations by introducing AlloyGAN, a framework combining LLM-assisted text mining with CGANs, which predicts thermodynamic properties for metallic glasses with less than 8% discrepancy from experiments.
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.