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

Teng Long; Nuno M. Fortunato; Yixuan Zhang; Oliver Gutfleisch; Hongbin Zhang

doi:10.1080/21663831.2020.1863876

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

Report | 更新时间：2026-08-19

- An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature
- Materials Research Letters Vol. 9, Issue 4, Pages: 169-174(2021)
- 作者机构：
  
  Institute of Materials Science Technische Universität of Darmstadt
- 作者简介：
- 基金信息：
- DOI：10.1080/21663831.2020.1863876
  CLC：
- Online First：11 January 2021，
  
  Published：2021
- 稿件说明：
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Teng Long, Nuno M. Fortunato, Yixuan Zhang, et al. An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature[J]. Materials Research Letters, 2021, 9(4): 169-174.
DOI：

Teng Long, Nuno M. Fortunato, Yixuan Zhang, et al. An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature[J]. Materials Research Letters, 2021, 9(4): 169-174. DOI： 10.1080/21663831.2020.1863876.

摘要

Abstract

BSTRACTMagnetic 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 (

) to classify ferromagnetic and antiferromagnetic compounds and to predict the

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.

关键词

Keywords

references

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Related Institution

College of Materials Science and Engineering State Key Laboratory of Cemented Carbide Hunan University

e SJTU-Paris Elite Institute of Technology Shanghai Jiao Tong University

b School of Materials Science and Engineering Shanghai Jiaotong University Shanghai People’s Republic of China

d Ningbo Zhongyuan Advanced Materials Technologies Co. Ltd.

c Department of Materials Science and Engineering The University of Tokyo

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