arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.06975cond-mat.mtrl-sci
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基于机器学习的Y型六方铁氧体非共线磁有序温度优化

Optimization of noncollinear magnetic ordering temperature in Y-type hexaferrite by machine learning

Yonghong Li, Jing Zhang, Linfeng Jiang, Long Zhang, Yugang Zhang, Xueliang Wu, Yisheng Chai, Xiaoyuan Zhou, Zizhen Zhou

AI总结:

针对Y型六方铁氧体最优掺杂组分难寻的问题,采用SISSO机器学习方法构建组分-性能描述符,预测并实验验证出高非共线磁转变温度的组分,为室温单相多铁材料开发提供新路径。

摘要:

寻找Y型六方铁氧体Ba₂Mg₂Fe₁₂O₂₂的最优掺杂组分以提升非共线磁转变温度(T_NC),是一项长期存在的挑战。本研究摒弃传统试错法,通过名为SISSO(确定独立性筛选与稀疏化算子)的数据驱动机器学习方法,构建了组分-性能描述符。基于筛选出的高效且具备物理解释性的描述符,研究预测了一系列具有高T_NC的Y型六方铁氧体组分,其中BaSrMg₀.₂₈Co₁.₇₂Fe₁₀Al₂O₂₂随后得到了实验验证。测试结果表明,在合适的外磁场条件下,该组分的T_NC最高可达568 K,其磁转变温度也提升至735 K。该工作为开发面向器件应用的室温单相多铁性材料提供了一条基于机器学习的路径。

英文摘要:

Searching the optimal doping compositions of the Y-type hexaferrite Ba2Mg2Fe12O22 remains a long-standing challenge for enhanced non-collinear magnetic transition temperature (TNC). Instead of the conventional trial-and-error approach, the composition-property descriptor is established via a data driven machine learning method named SISSO (sure independence screening and sparsifying operator). Based on the chosen efficient and physically interpretable descriptor, a series of Y-type hexaferrite compositions are predicted to hold high TNC, among which the BaSrMg0.28Co1.72Fe10Al2O22 is then experimentally validated. Test results indicate that, under appropriate external magnetic field conditions, the TNC of this composition reaches up to reaches up to 568 K, and its magnetic transition temperature is also elevated to 735 K. This work offers a machine learning-based route to develop room temperature single phase multiferroics for device applications.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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