ComProScanner: A multi-agent based framework for composition-property
  structured data extraction from scientific literature
Authors: name: Aritra Roy, name: Enrico Grisan, name: John Buckeridge, name: Chiara Gattinoni
Organization: South London Innovative Materials Evaluation Squad (SLIMES) Lab

Since the advent of various pre-trained large language models, extracting
structured knowledge from scientific text has experienced a revolutionary
change compared with traditional machine learning or natural language
processing techniques. Despite these advances, accessible automated tools that
allow users to construct, validate, and visualise datasets from scientific
literature extraction remain scarce. We therefore developed ComProScanner, an
autonomous multi-agent platform that facilitates the extraction, validation,
classification, and visualisation of machine-readable chemical compositions and
properties, integrated with synthesis data from journal articles for
comprehensive database creation. We evaluated our framework using 100 journal
articles against 10 different LLMs, including both open-source and proprietary
models, to extract highly complex compositions associated with ceramic
piezoelectric materials and corresponding piezoelectric strain coefficients
(d33), motivated by the lack of a large dataset for such materials.
DeepSeek-V3-0324 outperformed all models with a significant overall accuracy of
0.82. This framework provides a simple, user-friendly, readily-usable package
for extracting highly complex experimental data buried in the literature to
build machine learning or deep learning datasets.