Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning

This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstr...
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Autor*in:	Liu, Chengcheng [verfasserIn] Wang, Xuandong [verfasserIn] Cai, Weidong [verfasserIn] Su, Hang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Machine learning Amorphous alloy Magnetocaloric properties Alloy design

Übergeordnetes Werk:	Enthalten in: Journal of non-crystalline solids - Amsterdam [u.a.] : Elsevier Science, 1968, 625
Übergeordnetes Werk:	volume:625

DOI / URN:	10.1016/j.jnoncrysol.2023.122749

Katalog-ID:	ELV066263093

Internformat


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520			\|a This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications.
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650		4	\|a Amorphous alloy
650		4	\|a Magnetocaloric properties
650		4	\|a Alloy design
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700	1		\|a Cai, Weidong \|e verfasserin \|4 aut
700	1		\|a Su, Hang \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.jnoncrysol.2023.122749 doi (DE-627)ELV066263093 (ELSEVIER)S0022-3093(23)00614-2 DE-627 ger DE-627 rda eng 660 670 VZ 51.45 bkl 51.60 bkl 33.61 bkl 35.90 bkl Liu, Chengcheng verfasserin aut Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications. Machine learning Amorphous alloy Magnetocaloric properties Alloy design Wang, Xuandong verfasserin aut Cai, Weidong verfasserin aut Su, Hang verfasserin aut Enthalten in Journal of non-crystalline solids Amsterdam [u.a.] : Elsevier Science, 1968 625 Online-Ressource (DE-627)306659808 (DE-600)1500501-X (DE-576)096806486 0022-3093 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.45 Werkstoffe mit besonderen Eigenschaften VZ 51.60 Keramische Werkstoffe Hartstoffe Werkstoffkunde VZ 33.61 Festkörperphysik VZ 35.90 Festkörperchemie VZ AR 625
spelling	10.1016/j.jnoncrysol.2023.122749 doi (DE-627)ELV066263093 (ELSEVIER)S0022-3093(23)00614-2 DE-627 ger DE-627 rda eng 660 670 VZ 51.45 bkl 51.60 bkl 33.61 bkl 35.90 bkl Liu, Chengcheng verfasserin aut Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications. Machine learning Amorphous alloy Magnetocaloric properties Alloy design Wang, Xuandong verfasserin aut Cai, Weidong verfasserin aut Su, Hang verfasserin aut Enthalten in Journal of non-crystalline solids Amsterdam [u.a.] : Elsevier Science, 1968 625 Online-Ressource (DE-627)306659808 (DE-600)1500501-X (DE-576)096806486 0022-3093 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.45 Werkstoffe mit besonderen Eigenschaften VZ 51.60 Keramische Werkstoffe Hartstoffe Werkstoffkunde VZ 33.61 Festkörperphysik VZ 35.90 Festkörperchemie VZ AR 625
allfields_unstemmed	10.1016/j.jnoncrysol.2023.122749 doi (DE-627)ELV066263093 (ELSEVIER)S0022-3093(23)00614-2 DE-627 ger DE-627 rda eng 660 670 VZ 51.45 bkl 51.60 bkl 33.61 bkl 35.90 bkl Liu, Chengcheng verfasserin aut Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications. Machine learning Amorphous alloy Magnetocaloric properties Alloy design Wang, Xuandong verfasserin aut Cai, Weidong verfasserin aut Su, Hang verfasserin aut Enthalten in Journal of non-crystalline solids Amsterdam [u.a.] : Elsevier Science, 1968 625 Online-Ressource (DE-627)306659808 (DE-600)1500501-X (DE-576)096806486 0022-3093 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.45 Werkstoffe mit besonderen Eigenschaften VZ 51.60 Keramische Werkstoffe Hartstoffe Werkstoffkunde VZ 33.61 Festkörperphysik VZ 35.90 Festkörperchemie VZ AR 625
allfieldsGer	10.1016/j.jnoncrysol.2023.122749 doi (DE-627)ELV066263093 (ELSEVIER)S0022-3093(23)00614-2 DE-627 ger DE-627 rda eng 660 670 VZ 51.45 bkl 51.60 bkl 33.61 bkl 35.90 bkl Liu, Chengcheng verfasserin aut Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications. Machine learning Amorphous alloy Magnetocaloric properties Alloy design Wang, Xuandong verfasserin aut Cai, Weidong verfasserin aut Su, Hang verfasserin aut Enthalten in Journal of non-crystalline solids Amsterdam [u.a.] : Elsevier Science, 1968 625 Online-Ressource (DE-627)306659808 (DE-600)1500501-X (DE-576)096806486 0022-3093 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.45 Werkstoffe mit besonderen Eigenschaften VZ 51.60 Keramische Werkstoffe Hartstoffe Werkstoffkunde VZ 33.61 Festkörperphysik VZ 35.90 Festkörperchemie VZ AR 625
allfieldsSound	10.1016/j.jnoncrysol.2023.122749 doi (DE-627)ELV066263093 (ELSEVIER)S0022-3093(23)00614-2 DE-627 ger DE-627 rda eng 660 670 VZ 51.45 bkl 51.60 bkl 33.61 bkl 35.90 bkl Liu, Chengcheng verfasserin aut Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications. Machine learning Amorphous alloy Magnetocaloric properties Alloy design Wang, Xuandong verfasserin aut Cai, Weidong verfasserin aut Su, Hang verfasserin aut Enthalten in Journal of non-crystalline solids Amsterdam [u.a.] : Elsevier Science, 1968 625 Online-Ressource (DE-627)306659808 (DE-600)1500501-X (DE-576)096806486 0022-3093 nnns volume:625 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 51.45 Werkstoffe mit besonderen Eigenschaften VZ 51.60 Keramische Werkstoffe Hartstoffe Werkstoffkunde VZ 33.61 Festkörperphysik VZ 35.90 Festkörperchemie VZ AR 625
language	English
source	Enthalten in Journal of non-crystalline solids 625 volume:625
sourceStr	Enthalten in Journal of non-crystalline solids 625 volume:625
format_phy_str_mv	Article
bklname	Werkstoffe mit besonderen Eigenschaften Keramische Werkstoffe Hartstoffe Festkörperphysik Festkörperchemie
institution	findex.gbv.de
topic_facet	Machine learning Amorphous alloy Magnetocaloric properties Alloy design
dewey-raw	660
isfreeaccess_bool	false
container_title	Journal of non-crystalline solids
authorswithroles_txt_mv	Liu, Chengcheng @@aut@@ Wang, Xuandong @@aut@@ Cai, Weidong @@aut@@ Su, Hang @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	306659808
dewey-sort	3660
id	ELV066263093
language_de	englisch
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author	Liu, Chengcheng
spellingShingle	Liu, Chengcheng ddc 660 bkl 51.45 bkl 51.60 bkl 33.61 bkl 35.90 misc Machine learning misc Amorphous alloy misc Magnetocaloric properties misc Alloy design Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning
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topic_title	660 670 VZ 51.45 bkl 51.60 bkl 33.61 bkl 35.90 bkl Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning Machine learning Amorphous alloy Magnetocaloric properties Alloy design
topic	ddc 660 bkl 51.45 bkl 51.60 bkl 33.61 bkl 35.90 misc Machine learning misc Amorphous alloy misc Magnetocaloric properties misc Alloy design
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title	Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning
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title_full	Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning
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title_sort	prediction of magnetocaloric properties of fe-based amorphous alloys based on interpretable machine learning
title_auth	Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning
abstract	This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications.
abstractGer	This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications.
abstract_unstemmed	This study developed a machine learning model to accurately predict the isothermal magnetic entropy change (-SM) in amorphous alloys, a key parameter for evaluating magnetocaloric performance. Four machine learning algorithms were compared, and the (Extremely Randomized Trees) ETR algorithm demonstrated exceptional performance with an (R-squared) R2 value of 0.90 and a (Mean Absolute Percentage Error) MAPE of 13.31 % on the test set. Feature selection techniques, including Pearson correlation coefficient (PCC) and Recursive feature elimination (RFE), identified a subset of 7 important features: (Applied Field) Mf, δr, ΔH, ΔTm, ΔS, T m ‾ , and E c ‾ . The Shapley Additive Explanations (SHAP) method provided insights into feature importance and critical values. Design strategies for new alloys, using the FeZrB system as an example, were proposed based on the predictive model. The model's generalization ability was validated on other amorphous alloy systems, showcasing its wide applicability. This research contributes to the field of amorphous alloys and suggests future directions for machine learning applications.
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title_short	Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning
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doi_str	10.1016/j.jnoncrysol.2023.122749
up_date	2024-07-06T17:09:27.925Z
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Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning

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