Fine-Tuned Large Language Models for High-Accuracy Prediction of Band Gap and Stability in Transition Metal Sulfides.

This study presents a fine-tuned Large Language Model approach for predicting band gap and stability of transition metal sulfides. Our method processes textual descriptions of crystal structures directly, eliminating the need for complex feature engineering required by traditional ML and GNN approaches. Using a strategically selected dataset of 554 compounds from the Materials Project database, we fine-tuned GPT-3.5-turbo through nine consecutive iterations. Performance metrics improved significantly, with band gap prediction R values increasing from 0.7564 to 0.9989, while stability classification achieved F1 > 0.7751. The fine-tuned model demonstrated superior generalization ability compared to both GPT-3.5 and GPT-4.0 models, maintaining high accuracy across diverse material structures. This approach is particularly valuable for new material systems with limited experimental data, as it can extract meaningful features directly from text descriptions and transfer knowledge from pre-training to domain-specific tasks without relying on extensive numerical datasets.