Large Language Models for Superconductor Discovery (2512.10847v1)

Abstract: LLMs offer new opportunities for automated data extraction and property prediction across materials science, yet their use in superconductivity research remains limited. Here we construct a large experimental database of 78,203 records, covering 19,058 unique compositions, extracted from scientific literature using an LLM-driven workflow. Each entry includes chemical composition, critical temperature, measurement pressure, structural descriptors, and critical fields. We fine-tune several open-source LLMs for three tasks: (i) classifying superconductors vs. non-superconductors, (ii) predicting the superconducting transition temperature directly from composition or structure-informed inputs, and (iii) inverse design of candidate compositions conditioned on target Tc. The fine-tuned LLMs achieve performance comparable to traditional feature-based models and in some cases exceed them, while substantially outperforming their base versions and capturing meaningful chemical and structural trends. The inverse-design model generates chemically plausible compositions, including 28% novel candidates not seen in training. Finally, applying the trained predictors to the GNoME database identifies unreported materials with predicted Tc > 10 K. Although unverified, these candidates illustrate how integrating an LLM-driven workflow can enable scalable hypothesis generation for superconductivity discovery.