Dan Liu

Machine learning driven multi-property prediction for rare earth permanent magnet materials

Dan Liu, Yuan Li, Xinqi Zheng, Xin Ming et autres

Rare earth permanent magnets are key functional materials for strategic emerging fields such as energy engineering and intelligent manufacturing. Misch metal (MM)‑based alloys help boost rare‑earth resource utilization, yet traditional trial‑and‑error experiments suffer from long cycles and cumbersome variable control. In this …

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Data driven structure property relationship and rational design of rare-earth magnetostrictive materials

Dan Liu, Ying Tong, Yuan Li, Xinqi Zheng et autres

Abstract Rare-earth magnetostrictive materials possess irreplaceable strategic value in fields such as national defense, precision actuation, and smart sensing. Conventional materials development relies heavily on experimental trial-and-error approaches, first-principles calculations, and numerical simulations. However, owing to the complexity of multiphysics coupling, weak …

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Correlating misch metal content with magnetization reversal and coercivity in dual-alloy sintered magnets

Dan Liu, Jiefu Xiong, Qiong Wu, Xin Ming et autres

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Magnetocaloric effect of Tm0.2Y0.8Ga compound at liquid helium temperature

Zixiao Zhang, Jiahao Gao, Shuxian Yang, Lei Xi et autres

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Analysis and simulation of magnetocrystalline anisotropy of misch metal magnets

Jie-Fu Xiong, Dan Liu, Ruoshui Liu, Xin Ming et autres

2:14:1-type rare earth magnets play an indispensable role in many fields such as industry, military and electrical appliances because of their excellent hard magnetic performance. The magnetocrystalline anisotropy of magnetic materials is a crucial intrinsic parameter to ensure the high coercivity of …

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Characterization of interlayer coupling in YIG/Py bilayer using polarized neutron reflectometry

He Bai, Wei 为 He 何, Dan Liu, Jialiang 嘉亮 Li 李 et autres

Abstract The complex interplay of magnetic interactions at the yttrium iron garnet (YIG)/ferromagnet interface is important for spintronic and magnonic devices. In this study, we present a comprehensive investigation of the interlayer coupling and switching mechanisms in YIG/Py (permalloy) heterostructures based on …

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Observation of conflicting Seebeck coefficient and Hall coefficient in Sb-doped MnBi4Te7 crystals

Yinong Yin, Yan Zhang, Dan Liu, Jiazheng Hao et autres

In the past decades, great efforts have been devoted to designing and developing high-efficiency thermoelectric (TE) materials due to the increasing demands in the fields of power generation, cooling, and sensing. Given that the developments of TE materials based on charge and …

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Organic light-emitting transistors with high efficiency and narrow emission originating from intrinsic multiple-order microcavities

Zhagen Miao, Can Gao, Molin Shen, Peng Wang et autres

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Insight Into the Spin Reorientation Phase Transition in the Magnetocaloric NdNi Compound

Yawei Gao, Xinqi Zheng, Hui Wu, Juping Xu et autres

Abstract Exploring and comprehending magnetocaloric materials with spin reorientation (SR) phase transition is of vital importance for practical applications of magnetocaloric effect (MCE). Herein, this study presents a systematic study on the magnetic properties, heat transport properties, magnetic structure, and electronic structure …

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In-Solution Intramolecular Through-Space Conjugations of Sterically Constrained Tetranaphthylethane

Fei Gao, Yanning Xu, Liyuan Hou, Dan Liu et autres

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Effects of scanning strategy on the magnetic and mechanical properties of laser-based powder bed fused Nd-Fe-B magnets

Xin Ming, Xu Han, Jiantao Zhou, Jie-Fu Xiong et autres

In the past decades, laser-based powder bed fusion (LPBF) has been well-established for near-net-shape forming Nd-Fe-B magnets, efficiently employing the rare-earth elements (REE) and exhibiting the potential for directly fabricating magnets with complex shapes. In this work, four scanning strategies, including unidirectional, …

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Machine learning study on magnetic structure of rare earth based magnetic materials

Dan Liu, Zhixin Liu, Weiqiang Chen, Yinong Yin et autres

• 11 machine learning methods were trained to predict the material magnetic structure. • Rare earth content seriously affects the generation of nonlinear magnetic structure. • Neural Network performs best in skyrmion prediction, with accuracy 0.93 and reliability 97 %. • Skyrmion …

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