论文标题
稀土2-17-X磁铁的性能预测CE2FE17-XCOXCN：一项合并的机器学习和AB-Initio研究
Prediction on Properties of Rare-earth 2-17-X Magnets Ce2Fe17-xCoxCN : A Combined Machine-learning and Ab-initio Study
论文作者
论文摘要
我们采用了机器学习和第一原理计算的组合来预测稀土精益磁铁的磁性。为此，基于通过实验数据构建的训练集，对机器进行了训练，以对磁过渡温度（TC）进行预测，饱和磁化（μ0ms）的宽敞性以及磁晶方偶然动物（KU）的性质。随后，通过机器学习筛选的尚未合成化合物的μ0ms和ku的定量值由第一原理密度功能理论计算。在2-17-X磁铁，CE2FE17-XCOXCN上证明了提出的合并机器学习和第一原理计算技术的适用性。除了这项研究之外，我们通过计算小原子间质（N/C）的空置形成能来探索所提出的化合物的稳定性。我们的研究表明，拟议中的许多化合物提供了成为廉价且有效的永久磁铁溶液的可能性。
We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to make predictions on magnetic transition temperature (Tc), largeness of saturation magnetization (μ0Ms), and nature of the magnetocrystalline anisotropy (Ku). Subsequently, the quantitative values of μ0Ms and Ku of the yet-to-be synthesized compounds, screened by machine learning, are calculated by first-principles density functional theory. The applicability of the proposed technique of combined machine learning and first-principles calculations is demonstrated on 2-17-X magnets, Ce2Fe17-xCoxCN. Further to this study, we explore stability of the proposed compounds by calculating vacancy formation energy of small atom interstitials (N/C). Our study indicates a number of compounds in the proposed family, offers the possibility to become solution of cheap, and efficient permanent magnet.
