Work

Flexible, model-agnostic method for materials data extraction from text using general purpose language models

Year: 2024

Type: article

Abstract: This study presents an efficient language model-based method for high-precision data extraction from text, requiring minimal human effort.

Source: Digital Discovery

DOI: 10.1039/d4dd00016a

Authors: Maciej P. Polak, Shrey Modi, Anna Latosinska, Jinming Zhang, Ching-Wen Wang, Shaonan Wang, Ayan Deep Hazra, Dane Morgan

Institution: University of Wisconsin–Madison

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, Office of Advanced Cyberinfrastructure

Awards: Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure, Collaborative Research: CyberTraining: Implementation: Medium: The Informatics Skunkworks Program for Undergraduate Research at the Interface of Data Science and Materials Science, G4308630059

OpenAlex ID: W4398765347

FWCI: 3.592

Cites: 54

Cited by: 42

Related to: 10