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Machine learning and high-throughput screening algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys.

Number: 0026

Author(s) : BAIGUTLIN D., SOKOLOVSKIY V., BUCHELNIKOV V., TASKAEV S.

Summary

This paper examines the application of regression models using active machine learning techniques to predict the structural and magnetic properties, as well as to estimate the magnetocaloric effect of all-d metal Heusler alloys. The accuracy of the model was determined by cross-validation using the coefficient of determination R2 and the root mean square error RMSE. The model predictions were compared with experimental data and the results of density functional theory (DFT) calculations. The resulting regression model exhibits high accuracy for structural properties, although difficulties in predicting magnetic moments are noted due to the limited representation of magnetic states in the training dataset. The model is capable of qualitatively predicting martensitic transition stoppages for Ni-Co(Fe)-Mn-Ti systems. An improvement of the model could be achieved by extending the training data set to include other possible magnetic states and types of structural disorder.

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Details

  • Original title: Machine learning and high-throughput screening algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys.
  • Record ID : 30032634
  • Languages: English
  • Subject: Technology
  • Source: 10th IIR Conference on Caloric Cooling and Applications of Caloric Materials
  • Publication date: 2024/08/24
  • DOI: http://dx.doi.org/10.18462/iir.thermag.2024.0026

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