Machine learning models achieve 92% accuracy in classifying magnetic order in materials

Researchers have developed machine-learning classifiers capable of identifying magnetic order in materials with over 92% accuracy. These models, trained on experimental data and utilizing descriptors from the Materials Project Database, outperform previous studies and highlight a ferromagnetic bias within the Materials Project database. The developed classifiers can be used for large-scale screening of magnetic materials, aiding in the discovery of new materials with desired properties. AI

IMPACT Enhances the accuracy and trustworthiness of materials databases, accelerating the discovery of new materials with specific properties.

RANK_REASON Research paper detailing a new machine learning approach for materials science. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Ahmed Emad Fahmy
• arXiv
• density functional theory
• LightGBM
• Materials Project Database
• XGBoost

• paper
• model release

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