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

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- An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature
- An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature
- 材料研究快报（英文） 2021年9卷第4期页码：169-174
- 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
  中图分类号：
- 网络首发：2021-01-11，
  
  纸质出版：2021
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Teng Long, Nuno M. Fortunato, Yixuan Zhang, 等. An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature[J]. 材料研究快报（英文）, 2021,9(4):169-174.

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.
Teng Long, Nuno M. Fortunato, Yixuan Zhang, 等. An accelerating approach of designing ferromagnetic materials via machine learning modeling of magnetic ground state and Curie temperature[J]. 材料研究快报（英文）, 2021,9(4):169-174. DOI： 10.1080/21663831.2020.1863876.

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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