An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature

Abstract

Magnetic materials have a plethora of applications from information technologies to energy harvesting. However, their functionalities are often limited by the magnetic ordering temperature. In this work, we performed random forest on the magnetic ground state and the Curie temperature (TC) to classify ferromagnetic and antiferromagnetic compounds and to predict the TC of the ferromagnets. The resulting accuracy is about 87% for classification and 91% for regression. When the trained model is applied to magnetic intermetallic materials in Materials Project, the accuracy is comparable. Our work paves the way to accelerate the discovery of new magnetic compounds for technological applications.

Keywords

• magnetic ground state
• curie temperature
• machine learning
• random forest