# LLM-based extraction of experimental parameters from scientific text

Screening superconductors has long rested on a single experimental parameter, the critical temperature, while quantities that decide usefulness, such as the upper critical field and critical current density, sit unstructured in the literature. AutoSuperCon uses a large language model to extract eight experimental superconducting parameters from more than 47,000 open arXiv preprints in the superconductivity category, adds digitized entries from the Roberts 1976 survey, and yields 20,354 entries for 6,793 superconductors. On the 2,439 materials shared with NIMS SuperCon, critical temperatures show a median deviation of 0.25 K, or 3.5% relative; the database supports large-scale tests of empirical relations and composition-based prediction of critical temperature and upper critical field.

Screening superconductors has long rested on a single experimental parameter, the critical temperature, while quantities that decide usefulness, such as the upper critical field and critical current density, sit unstructured in the literature. AutoSuperCon uses a large language model to extract eight experimental superconducting parameters from more than 47,000 open arXiv preprints in the superconductivity category, adds digitized entries from the Roberts 1976 survey, and yields 20,354 entries for 6,793 superconductors. On the 2,439 materials shared with NIMS SuperCon, critical temperatures show a median deviation of 0.25 K, or 3.5% relative; the database supports large-scale tests of empirical relations and composition-based prediction of critical temperature and upper critical field.