Korean Journal of Materials Research (한국재료학회지)

Materials Research Society of Korea (한국재료학회)
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Prediction of Transition Temperature and Magnetocaloric Effects in Bulk Metallic Glasses with Ensemble Models

앙상블 기계학습 모델을 이용한 비정질 소재의 자기냉각 효과 및 전이온도 예측

  • Chunghee Nam (Department of Electrical and Electronic Engineering, Hannam University)
  • 남충희 (한남대학교 전기전자공학과)
  • Received : 2024.05.23
  • Accepted : 2024.06.27
  • Published : 2024.07.27
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Abstract

In this study, the magnetocaloric effect and transition temperature of bulk metallic glass, an amorphous material, were predicted through machine learning based on the composition features. From the Python module 'Matminer', 174 compositional features were obtained, and prediction performance was compared while reducing the composition features to prevent overfitting. After optimization using RandomForest, an ensemble model, changes in prediction performance were analyzed according to the number of compositional features. The R2 score was used as a performance metric in the regression prediction, and the best prediction performance was found using only 90 features predicting transition temperature, and 20 features predicting magnetocaloric effects. The most important feature when predicting magnetocaloric effects was the 'Fe' compositional ratio. The feature importance method provided by 'scikit-learn' was applied to sort compositional features. The feature importance method was found to be appropriate by comparing the prediction performance of the Fe-contained dataset with the full dataset.

Keywords

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (NRF-2021R 1F1A1052971) and 2023 Hannam University Research Fund.

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