Automated construction of inorganic materials databases and materials discovery based on large language models

Abstract

The rapid expansion of materials science literature demands scalable and intelligent systems for extracting, structuring, and utilizing scientific knowledge. Traditional manual approaches to inorganic materials database construction are labor-intensive and error-prone. In this study, we propose a novel end-to-end framework that leverages instruction-tuned large language models (LLMs) for automated knowledge extraction and discovery in inorganic materials science. By fine-tuning LLMs on domain-specific corpora—including peer-reviewed articles, patents, and chemical databases—the system accurately extracts structured material-property-synthesis relationships from unstructured text. These records are aligned to a schema and stored in a queryable knowledge graph. Furthermore, we demonstrate inverse design by prompting LLMs to generate candidate materials satisfying user-defined targets (e.g., high thermal conductivity). Evaluations on benchmark synthesis corpora show high accuracy in named entity recognition (F1-score > 95%) and low numerical error for temperature/duration extraction. The resulting database supports effective downstream applications such as Curie temperature prediction (RMSE = 23.6 K, R_^2 = 0.927). This work showcases how LLMs, when combined with schema-aware reasoning and human-in-the-loop curation, can serve as robust tools for accelerating autonomous materials discovery.