Effect of Yttrium Doping on Magnetic Properties and Magnetic Entropy Change of Bilayered Perovskite La1.3Sr1.7Mn2O7

Cao, Fengze; Liu, Yi; Wu, Haiming; Xu, Bo; Xiang, Jin; Zhao, Jianjun; Sun, Xi

doi:10.1007/s10909-020-02457-8

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…The latest development of magnetic refrigeration technology based on the magnetocaloric effect of magnetic materials (especially near room temperature) has provided a promising alternative to vapor compression refrigeration 81,82 . In order to design a magnetic refrigerator with an operating temperature close to room temperature, much attention has been paid to the magnetocaloric material with a large maximum magnetic entropy change (MMEC) over a wide temperature range 83,84 . Zhang et al 85 established a GPR model to elucidate the statistical relationship between the MMEC and lattice parameters of magnetocaloric lanthanum manganite perovskites.…”

Section: Applications Of Machine Learning In Perovskite Materialsmentioning

confidence: 99%

Machine learning for perovskite materials design and discovery

et al. 2021

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The development of materials is one of the driving forces to accelerate modern scientific progress and technological innovation. Machine learning (ML) technology is rapidly developed in many fields and opening blueprints for the discovery and rational design of materials. In this review, we retrospected the latest applications of ML in assisting perovskites discovery. First, the development tendency of ML in perovskite materials publications in recent years was organized and analyzed. Second, the workflow of ML in perovskites discovery was introduced. Then the applications of ML in various properties of inorganic perovskites, hybrid organic–inorganic perovskites and double perovskites were briefly reviewed. In the end, we put forward suggestions on the future development prospects of ML in the field of perovskite materials.

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