The Northeast Materials Database for Magnetic Materials

TL;DR

This paper introduces NEMAD, a comprehensive magnetic materials database created using LLMs, enabling machine learning models to classify materials and predict transition temperatures, thereby accelerating magnetic materials discovery.

Contribution

The study develops NEMAD, a large experiment-based magnetic materials database, and demonstrates machine learning models for classification and temperature prediction with high accuracy.

Findings

Achieved 90% accuracy in classifying magnetic materials.

Predicted Curie and Ne9el temperatures with R2 of 0.87 and 0.83.

Identified numerous high-temperature magnetic material candidates.

Abstract

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries(www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (N\'eel) temperature with a coefficient of determination (R2) 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 (N\'eel) 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.