ComProScanner: A multi-agent based framework for composition-property
  structured data extraction from scientific literature
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.
68fb47f41ff1070cf1666d8e
Aritra Roy
claimed_verified
2025-10-24T15:59:17.823Z
65e62a6e9e8bda232c34a309
https://cdn-avatars.huggingface.co/v1/production/uploads/65e62a6e9e8bda232c34a309/jAW1xVD0tp0dweq7RVGNf.jpeg
Aritra Roy
user
aritraroy24
68fb47f41ff1070cf1666d8f
Enrico Grisan
68fb47f41ff1070cf1666d90
John Buckeridge
68fb47f41ff1070cf1666d91
Chiara Gattinoni
ComProScanner: A multi-agent based framework for composition-property
  structured data extraction from scientific literature
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.
ComProScanner, an autonomous multi-agent platform, extracts, validates, classifies, and visualizes chemical compositions and properties from scientific literature, outperforming various LLMs in accuracy.
pre-trained large language models machine-readable chemical compositions properties synthesis data journal articles DeepSeek-V3-0324 piezoelectric materials piezoelectric strain coefficients machine learning deep learning datasets