Work

The northeast materials database for magnetic materials

Year: 2025

Type: article

Abstract: ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Source: Nature Communications

DOI: 10.1038/s41467-025-64458-z

Authors: Suman Itani, Yibo Zhang, Jiadong Zang

Institution: University of New Hampshire

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $6,990

Funders: U.S. Department of Energy, Basic Energy Sciences, Division of Materials Sciences and Engineering