Search papers, labs, and topics across Lattice.
The paper introduces Material Database Agent (MDA), a multi-agent system designed to automate the extraction of structured data from materials science literature. MDA processes PDFs in parallel, using specialized sub-agents to extract information from text and figures, and then compiles these into a unified database. This approach overcomes the limitations of manual database construction and single-pass extraction pipelines, paving the way for scalable scientific database creation.
Automating materials science database construction is now feasible: a multi-agent system extracts structured data from scientific literature with high speed and accuracy.
Materials science workflows rely on structured and unstructured data from the vast body of available scientific literature. However, most of the experimental details remain buried in text, tables, graphs and figures. Thus, constructing databases that incorporate this data is a manual, time-consuming, and hard-to-scale process. Multimodal large language models have made it feasible to extract information from text and scientific figures with high speed and accuracy. This opens the possibility of an AI system that can create production-scale material databases. Material Database Agent (MDA) is a modular, multi-agent system architecture for converting research literature into structured databases. MDA accepts article PDFs as input, which are subsequently processed in parallel into markdown files and figures. Multiple sub-agents read these markdown files and figures in parallel to assemble sub-databases for each paper. These sub-databases are then compiled into a single tabular database by an agent. As opposed to using either a rule-based approach or a single-pass pipeline for extracting information, MDA is a specialized architecture for transforming the literature into a database in the field of materials science. More generally, this study provides a basis for positioning multimodal agentic information extraction as a viable means for constructing next-generation scientific databases from the primary literature.