Cross-Lingual Material Property Discovery Using Graph Neural Networks and Language Models

The prediction of material properties is increasingly crucial for materials science, enabling design optimization and cost reduction. This study explores a novel approach: leveraging large language models (LLMs) to generate stable inorganic materials directly as text. Rather than predicting properties, we focus on generating plausible crystal structures encoded as text strings. We further investigate the performance differences between Chinese and English LLMs in this domain and demonstrate the capability of fine-tuning LLaMA-2 with few sample learning, presenting its scale learning curve. Our approach highlights the potential of LLMs to capture intricate structural information and generate novel material candidates, paving a new path for AI-driven materials discovery.