Lecture image placeholder

Premium content

Access to this content requires a subscription. You must be a premium user to view this content.

Monthly subscription - $9.99Pay per view - $4.99Access through your institutionLogin with Underline account
Need help?
Contact us
Lecture placeholder background
VIDEO DOI: https://doi.org/10.48448/ycmd-2881

technical paper

MMM 2022

November 07, 2022

Minneapolis, United States

Machine learning assisted optimization towards high performance La(Fe,Si)13

La(Fe,Si)13-based (1:13) compounds exhibit giant magnetocaloric effect and are promising candidates for room temperature as well as cryogenic magnetic refrigeration 1,2. The magnetic refrigeration materials require a comprehensive evaluation of their properties such as transition temperature (Ttr), entropy change (△Sm), adiabatic temperature change (△Tad), thermal hysteresis (△Thys), relative cooling power (RCP), thermal conductivity and mechanical stability 3. Extensive studies on tuning these properties have been carried out for the 1:13 system by varying the chemical compositions and processing conditions. However, an excellent combination of properties has not been achieved yet, hindering any practical application 4. In this work, we employed machine learning (ML) to address the optimization problem in the 1:13 system. A dataset containing over 1000 samples in total was collected using data mining across 200 articles. Several machine learning models were trained to predict Ttr, △Sm, RCP and Thys as targets and compared using the root mean squared error (RMSE) metric. We found that random forest (RF) and gradient boosting (GB) regressors outperformed other models such as decision tree and k-NN regressors. The achieved performance of the former model is demonstrated in Fig. 1 for the case of predicting Ttr. The relative feature importance extracted from the trained RF model in Fig. 2 shows the important parameters for tuning the Ttr, while the corresponding Pearson correlation co-efficients (PCC) shows how these parameters influence the Ttr. Similarly, the critical parameters were found out for the other target properties and used for further optimization study. The compositional optimization of the 1:13 system was performed by means of the differential evolution technique with a multiobjective target toward the best combination of the magnetocaloric properties.


  1. A. Fujita et al., Phys. Rev. B, 67 (2003) 104416.
  2. S. Fujieda et al., Appl. Phys. Lett., 89 (2006) 062504.
  3. V. Franco et al., Annual Review of Materials Research, 42 (2012) 305-342.
  4. V. Paul-Boncour et al., Magnetochemistry, 7 (2021) 13.


Transcript English (automatic)

Next from MMM 2022

Topological features in real and reciprocal space: a case study of metallic ferrimagnet Mn4N
technical paper

Topological features in real and reciprocal space: a case study of metallic ferrimagnet Mn4N

MMM 2022

Temuujin Bayaraa and 1 other author

07 November 2022

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)


  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved