• Bernthaler, Timo
• Schneider, Gerhard
• Kini, Anoop
• Choudhary, Amit Kumar
• Jansche, Andreas
• Hohs, Dominic
• Csiszár, Dr. Orsolya
• Goll, Dagmar

Abstract

<jats:p> The TM<jats:sub>14</jats:sub>RE<jats:sub>2</jats:sub>B-based phases (TM = transition metal, RE = rare earth metal; hereafter called 14:2:1) enable permanent magnets with outstanding magnetic properties. Novel chemical compositions that represent new 14:2:1 phases necessitate that they do not demagnetize at application-specific operating temperatures. Therefore, an accurate knowledge of the Curie temperature ( T<jats:sub> c</jats:sub>) is important. For magnetic 14:2:1 phases, we present a machine learning model that predicts T<jats:sub> c</jats:sub> by using merely chemical compositional features. Hyperparameter tuning on bagging and boosting models, as well as averaging predictions from individual models using the voting regressor, enables a low mean-absolute-error of 16 K on an unseen test set. The training set and a test set have been constructed by randomly splitting, in an 80:20 ratio, of a database that contains 449 phases (270 compositionally unique) mapped with their T<jats:sub> c</jats:sub>, taken from distinct publications. The model correctly identifies the relative importance of key substitutional elements that influence T<jats:sub> c</jats:sub>, especially in an Fe base such as Co, Mn, and Al. This paper is expected to serve as a basis for accurate Curie temperature predictions in the sought-after 14:2:1 permanent magnet family, particularly for transition metal substitution of within 20% in an Fe or Co base. </jats:p>

Topics

• impedance spectroscopy
• phase
• chemical composition
• machine learning
• Curie temperature
• rare earth metal