Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models. (arXiv:2302.04914v1 [cond-mat.mtrl-sci])

Accurate and comprehensive material databases extracted from research papers
are critical for materials science and engineering but require significant
human effort to develop. In this paper we present a simple method of extracting
materials data from full texts of research papers suitable for quickly
developing modest-sized databases. The method requires minimal to no coding,
prior knowledge about the extracted property, or model training, and provides
high recall and almost perfect precision in the resultant database. The method
is fully automated except for one human-assisted step, which typically requires
just a few hours of human labor. The method builds on top of natural language
processing and large general language models but can work with almost any such
model. The language models GPT-3/3.5, bart and DeBERTaV3 are evaluated here for
comparison. We provide a detailed detailed analysis of the methods performance
in extracting bulk modulus data, obtaining up to 90% precision at 96% recall,
depending on the amount of human effort involved. We then demonstrate the
methods broader effectiveness by developing a database of critical cooling
rates for metallic glasses.