Northeast Materials Database (NEMAD): Enabling Discovery of High Transition Temperature Magnetic Compounds

• URL: http://arxiv.org/abs/2409.15675v1
• Date: Tue, 24 Sep 2024 02:27:10 GMT
• Title: Northeast Materials Database (NEMAD): Enabling Discovery of High Transition Temperature Magnetic Compounds
• Authors: Suman Itani, Yibo Zhang, Jiadong Zang,
• Abstract summary: This study uses Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database. The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Machine learning models were developed to classify materials and predict transition temperatures.
• Score: 1.1856958240619673
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The discovery of novel magnetic materials with greater operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are challenging and limited due to the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by introducing a new approach that uses Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 26,706 magnetic materials (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, machine learning models were developed 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 0.86 (0.85) and a mean absolute error (MAE) of 62K (32K). These models identified 62 (19) 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 in accelerating the discovery of magnetic materials.

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