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 (T_C ) to classify ferromagnetic and antiferromagnetic compounds and to predict the T_C 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.

摘要

磁性材料在信息技术到能量收集方面的应用非常广泛。但是，它们的功能通常受到磁性订购温度的限制。在这项工作中，我们对磁性基态和居里温度（T _C ）进行了随机森林分析，以对铁磁和反铁磁化合物进行分类，并预测铁磁体的T _C。 最终的分类精度约为87％，回归精度约为91％。在材料项目中将训练后的模型应用于金属间磁性材料时，精度是可比的。我们的工作为加速发现用于技术应用的新型磁性化合物铺平了道路。