# Inverse Materials Design by Large Language Model-Assisted Generative Framework

arXiv: https://arxiv.org/abs/2502.18127 · HTML: https://mlsanity.com/paper/2502.18127/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2025.

## Built on by (most cited first, 1 of 1)

- [Benchmarking Generative AI Against Bayesian Optimization for Constrained Multi-Objective Inverse Design](https://mlsanity.com/paper/2511.00070/) (2025)

## Abstract (arXiv)

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.