AutoSchema: Self-Prompted Schema Induction and Evidence-Grounded Extraction for Materials Science Literature

Scientific papers in materials science contain critical experimental details (e.g., reagents, synthesis conditions, and measured properties) for building structured databases and enabling downstream analysis, but large-scale structured extraction remains difficult. Classic rule-based systems rely on hand-written patterns and pre-defined targets, which can be brittle when high-value fields are domain-specific or shift across subdomains. Recent LLM-based extraction is more flexible, but it often still depends on manual schema and prompt design, where a schema specifies the target fields to extract into a table. We present AutoSchema, an iterative literature-mining pipeline that bootstraps both retrieval and schema induction from a small set of seed papers, then freezes the induced schema for scalable, evidence-grounded extraction. AutoSchema is not fixed-field extraction with a manually specified field list (e.g., ChatExtract-style prompting), not entity/ontology-style recognition and normalization (e.g., MatScholar), and not rule-based pattern engineering. Instead, it targets scalable literature mining under schema uncertainty, where the schema must be induced, stabilized, reused, and audited. Starting from 3–5 seed PDFs, AutoSchema expands a domain corpus via multi-source retrieval, induces and refines a compact schema set across rounds, and performs schema-conditioned, evidence-grounded extraction with JSONL audit logs and flattened tables. Across five independent seed sets, lightweight canonicalization and semantic normalization substantially improve schema agreement (mean pairwise Jaccard similarity from 19.97% to 43.41%; field-level overlap from 32.50% to 59.91%). In a covalent organic frameworks (COF) case study, Phase I expands the literature to a 1,656-paper corpus and Phase II applies the frozen schema to a 200-paper sample. Compared to ChemDataExtractor, AutoSchema improves coverage and yields more verifiable extractions; compared to an evidence-grounded ChatExtract-style baseline with a manually specified field list, AutoSchema provides stronger evidence support, while fixed-field prompting can reach higher fill rate and domain-specific coverage when the target schema is known. Additional small-scale pilot tests run the freeze-and-reuse pipeline on MOF/ZIF and Alzheimer's disease amyloid/APP processing, and query-generation/retrieval tests on prompt-engineering literature, illustrating AutoSchema's transferability while keeping COF as the main benchmark.