Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on ...Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.展开更多
Effects of alloying processing on tensile test properties of Fe 3Al based alloys have been studied. Results show that microalloying of cerium is very effective on increasing the room temperature ductility of Fe 3Al...Effects of alloying processing on tensile test properties of Fe 3Al based alloys have been studied. Results show that microalloying of cerium is very effective on increasing the room temperature ductility of Fe 3Al based alloys. Surface analysis by XPS demonstrates that cerium addition causes the change in the oxide chemistry and provides rapid passivation of the specimen surface. The high temperature strength and creep resistance of Fe 3Al based alloys can be significantly enhanced by alloying additions of tungsten, niobium or molybdenum, especially when combined additions of tungsten with niobium or molybdenum are used. The additions of tungsten, niobium or molybdenum also result in the significant microstructural refinement and the formation of fine precipitates which are identified as M 6C type carbide in the alloys containing tungsten.展开更多
基金supported by the National Basic Research Program of China(Grant No.2014CB643702)the National Natural Science Foundation of China(Grant No.51590880)+1 种基金the Knowledge Innovation Project of the Chinese Academy of Sciences(Grant No.KJZD-EW-M05)the National Key Research and Development Program of China(Grant No.2016YFB0700903)
文摘Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.
文摘Effects of alloying processing on tensile test properties of Fe 3Al based alloys have been studied. Results show that microalloying of cerium is very effective on increasing the room temperature ductility of Fe 3Al based alloys. Surface analysis by XPS demonstrates that cerium addition causes the change in the oxide chemistry and provides rapid passivation of the specimen surface. The high temperature strength and creep resistance of Fe 3Al based alloys can be significantly enhanced by alloying additions of tungsten, niobium or molybdenum, especially when combined additions of tungsten with niobium or molybdenum are used. The additions of tungsten, niobium or molybdenum also result in the significant microstructural refinement and the formation of fine precipitates which are identified as M 6C type carbide in the alloys containing tungsten.
基金supported by the National Natural Science Foundation,China (No.52074131)the National Key R&D Project,China (No.2022YFC3900500)+2 种基金the International Technology Cooperation Program of Guangdong Academy of Sciences,China (No.2020GDASYL-20200504001)the Open Competition to Select the Best Candidate of Shangrao,China (No.2021A005)the BL13HB beamline of Shanghai Synchrotron Radiation Facility (SSRF)for providing synchrotron radiation beamtime (Nos.2020-SSRF-PT-011937,2021-SSRF-PT-017645).
