Magnetocaloric performance is of vital importance for Mn-Fe-P-Si alloys.However,when processes and compositions are considered,designing alloys with large magnetic entropy changes(ΔS_(m)),low thermal hysteresis(ΔT_(...Magnetocaloric performance is of vital importance for Mn-Fe-P-Si alloys.However,when processes and compositions are considered,designing alloys with large magnetic entropy changes(ΔS_(m)),low thermal hysteresis(ΔT_(hys)),and Curie temperatures(T_(C))around room temperature become relatively complicated.In this study,we adopt machine learning methods to predict the magnetocaloric performance of Mn-Fe-P-Si compounds for the first time.To achieve this goal,503,465,and 660 data points for datasets with T_(C),ΔT_(hys),andΔS_(m)are collected,respectively.The collected datasets contain parameters of compositions,preparations,heat treatment,and magnetic field changes.We search for the optimal configuration using various methods and also compare their mean squared errors(MSE)and allowable errors.Evaluation results show that the performance of neural networks(NNs)is better than other methods.Therefore,we select NN to explore the T_(C),ΔT_(hys),andΔS_(m)values as a function of Mn,Si,metal/non-metal ratios,and B(Boron).We also propose to use the composition window with excellent magnetocaloric performance.These results not only help us gain deep insights into Mn-Fe-P-Si alloys but also accelerate the design process of alloys suitable for magnetocaloric materials.This work has the potential to solve the challenges and boost the research of Mn-Fe-P-Si alloys.展开更多
LaFe13-x Mx(M=Si,Al)alloys are promising for use in magnetic refrigeration.However,they require long annealing time(30 days)in order to optimize the magnetocaloric properties.Research has shown that the addition of ex...LaFe13-x Mx(M=Si,Al)alloys are promising for use in magnetic refrigeration.However,they require long annealing time(30 days)in order to optimize the magnetocaloric properties.Research has shown that the addition of extra La in off-stoichiometric alloys can greatly shorten the annealing time.Therefore,the purpose of this study is to investigate the influence of the extra addition of La on the annealing properties of a new off-stoichiometric La1.7Fe11.6Al1.4-xSix(x=0,0.1,0.4)alloys.It was demonstrated that after a 36 h annealing time,a large volume fraction of 1:13 magnetocaloric phase was obtained for all alloys.Further microstructural analysis of the off-stoichiometric La1.7Fe11.6Al1.4-xSix alloys revealed a facet-like grain morphology.The La1.7Fe11.6Al1.4 and La1.7Fe11.6Al1Si0.4 alloys were shown to contain large 1:13 phase precipitates separated in a La-rich matrix,while the La1.7Fe11.6Al1.3Si0.1 alloy had a continuous 1:13 phase matrix with a fine dispersion of La-rich precipitates throughout.When the magnetic field varied between 0 and 2 T,the corresponding magnetic entropy change and relative cooling capacity for the La1.7Fe11.6Al1.3Si0.1 specimen were determined as 4.58 J/kg K and 173.6 J/kg,respectively.More importantly,the La1.7Fe11.6Al1.3Si0.1 alloy displayed only a slight volume change when the meta-magnetic phase transition occurred,which is promising for cyclic use.展开更多
基金financially supported by the National Natural Science Foundation of China(Nos.52074182 and 51821001)。
文摘Magnetocaloric performance is of vital importance for Mn-Fe-P-Si alloys.However,when processes and compositions are considered,designing alloys with large magnetic entropy changes(ΔS_(m)),low thermal hysteresis(ΔT_(hys)),and Curie temperatures(T_(C))around room temperature become relatively complicated.In this study,we adopt machine learning methods to predict the magnetocaloric performance of Mn-Fe-P-Si compounds for the first time.To achieve this goal,503,465,and 660 data points for datasets with T_(C),ΔT_(hys),andΔS_(m)are collected,respectively.The collected datasets contain parameters of compositions,preparations,heat treatment,and magnetic field changes.We search for the optimal configuration using various methods and also compare their mean squared errors(MSE)and allowable errors.Evaluation results show that the performance of neural networks(NNs)is better than other methods.Therefore,we select NN to explore the T_(C),ΔT_(hys),andΔS_(m)values as a function of Mn,Si,metal/non-metal ratios,and B(Boron).We also propose to use the composition window with excellent magnetocaloric performance.These results not only help us gain deep insights into Mn-Fe-P-Si alloys but also accelerate the design process of alloys suitable for magnetocaloric materials.This work has the potential to solve the challenges and boost the research of Mn-Fe-P-Si alloys.
基金financially supported by the National Key R&D Program of China (Nos. 2016YFB0701204 and 2017YFB0305300)the National Natural Science Foundation of China (Nos. 51774201 and 51727802)the National Natural Science Foundation of China-Excellent Young Scholars (No. 51922068)。
文摘LaFe13-x Mx(M=Si,Al)alloys are promising for use in magnetic refrigeration.However,they require long annealing time(30 days)in order to optimize the magnetocaloric properties.Research has shown that the addition of extra La in off-stoichiometric alloys can greatly shorten the annealing time.Therefore,the purpose of this study is to investigate the influence of the extra addition of La on the annealing properties of a new off-stoichiometric La1.7Fe11.6Al1.4-xSix(x=0,0.1,0.4)alloys.It was demonstrated that after a 36 h annealing time,a large volume fraction of 1:13 magnetocaloric phase was obtained for all alloys.Further microstructural analysis of the off-stoichiometric La1.7Fe11.6Al1.4-xSix alloys revealed a facet-like grain morphology.The La1.7Fe11.6Al1.4 and La1.7Fe11.6Al1Si0.4 alloys were shown to contain large 1:13 phase precipitates separated in a La-rich matrix,while the La1.7Fe11.6Al1.3Si0.1 alloy had a continuous 1:13 phase matrix with a fine dispersion of La-rich precipitates throughout.When the magnetic field varied between 0 and 2 T,the corresponding magnetic entropy change and relative cooling capacity for the La1.7Fe11.6Al1.3Si0.1 specimen were determined as 4.58 J/kg K and 173.6 J/kg,respectively.More importantly,the La1.7Fe11.6Al1.3Si0.1 alloy displayed only a slight volume change when the meta-magnetic phase transition occurred,which is promising for cyclic use.
