ArticleMaterials (Basel, Switzerland)2025
Fine-Tuned Large Language Models for High-Accuracy Prediction of Band Gap and Stability in Transition Metal Sulfides.
Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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
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