Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design
Stimulated by a recent report of a giant magnetocaloric effect in HoB2 found via machine-learning predictions, we have explored the magnetocaloric properties of a related compound ErB2 that has remained the last ferromagnetic material among the rare-earth diboride (REB2) family with unreported magne...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
|
| Series: | Science and Technology of Advanced Materials: Methods |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/27660400.2023.2217474 |
| _version_ | 1827818549984362496 |
|---|---|
| author | Kensei Terashima Pedro Baptista de Castro Akiko Takahashi Saito Takafumi D Yamamoto Ryo Matsumoto Hiroyuki Takeya Yoshihiko Takano |
| author_facet | Kensei Terashima Pedro Baptista de Castro Akiko Takahashi Saito Takafumi D Yamamoto Ryo Matsumoto Hiroyuki Takeya Yoshihiko Takano |
| author_sort | Kensei Terashima |
| collection | DOAJ |
| description | Stimulated by a recent report of a giant magnetocaloric effect in HoB2 found via machine-learning predictions, we have explored the magnetocaloric properties of a related compound ErB2 that has remained the last ferromagnetic material among the rare-earth diboride (REB2) family with unreported magnetic entropy change $|{\rm \Delta} {S_M}|$. The evaluated $|{\rm \Delta} {S_M}|$ at field change of 5 T in ErB2 turned out to be as high as 26.1 J kg−1 K−1 around the ferromagnetic transition (${T_C}$) of 14 K. In this series, HoB2 is found to be the material with the largest $|{\rm \Delta} {S_M}|$ as the model predicted, while the predicted values showed a deviation with a systematic error compared to the experimental values. Through a coalition analysis using SHAP, we explore how this rare-earth dependence and the deviation in the prediction are deduced in the model. We further discuss how SHAP analysis can be useful in clarifying favorable combinations of constituent atoms through the machine-learned model with compositional descriptors. This analysis helps us to perform materials design with aid of machine learning of materials data. |
| first_indexed | 2024-03-12T00:56:26Z |
| format | Article |
| id | doaj.art-a1e7ad9fc27447b4ba79336b35ff937c |
| institution | Directory Open Access Journal |
| issn | 2766-0400 |
| language | English |
| last_indexed | 2024-03-12T00:56:26Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials: Methods |
| spelling | doaj.art-a1e7ad9fc27447b4ba79336b35ff937c2023-09-14T13:24:39ZengTaylor & Francis GroupScience and Technology of Advanced Materials: Methods2766-04002023-12-013110.1080/27660400.2023.22174742217474Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials designKensei Terashima0Pedro Baptista de Castro1Akiko Takahashi Saito2Takafumi D Yamamoto3Ryo Matsumoto4Hiroyuki Takeya5Yoshihiko Takano6National Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceStimulated by a recent report of a giant magnetocaloric effect in HoB2 found via machine-learning predictions, we have explored the magnetocaloric properties of a related compound ErB2 that has remained the last ferromagnetic material among the rare-earth diboride (REB2) family with unreported magnetic entropy change $|{\rm \Delta} {S_M}|$. The evaluated $|{\rm \Delta} {S_M}|$ at field change of 5 T in ErB2 turned out to be as high as 26.1 J kg−1 K−1 around the ferromagnetic transition (${T_C}$) of 14 K. In this series, HoB2 is found to be the material with the largest $|{\rm \Delta} {S_M}|$ as the model predicted, while the predicted values showed a deviation with a systematic error compared to the experimental values. Through a coalition analysis using SHAP, we explore how this rare-earth dependence and the deviation in the prediction are deduced in the model. We further discuss how SHAP analysis can be useful in clarifying favorable combinations of constituent atoms through the machine-learned model with compositional descriptors. This analysis helps us to perform materials design with aid of machine learning of materials data.http://dx.doi.org/10.1080/27660400.2023.2217474magnetocaloric materialsmachine learningdata-driven materials search |
| spellingShingle | Kensei Terashima Pedro Baptista de Castro Akiko Takahashi Saito Takafumi D Yamamoto Ryo Matsumoto Hiroyuki Takeya Yoshihiko Takano Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design Science and Technology of Advanced Materials: Methods magnetocaloric materials machine learning data-driven materials search |
| title | Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design |
| title_full | Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design |
| title_fullStr | Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design |
| title_full_unstemmed | Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design |
| title_short | Experimental exploration of ErB2 and SHAP analysis on a machine-learned model of magnetocaloric materials for materials design |
| title_sort | experimental exploration of erb2 and shap analysis on a machine learned model of magnetocaloric materials for materials design |
| topic | magnetocaloric materials machine learning data-driven materials search |
| url | http://dx.doi.org/10.1080/27660400.2023.2217474 |
| work_keys_str_mv | AT kenseiterashima experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign AT pedrobaptistadecastro experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign AT akikotakahashisaito experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign AT takafumidyamamoto experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign AT ryomatsumoto experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign AT hiroyukitakeya experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign AT yoshihikotakano experimentalexplorationoferb2andshapanalysisonamachinelearnedmodelofmagnetocaloricmaterialsformaterialsdesign |