论文标题
使用卷积神经网络学习水晶场参数
Learning crystal field parameters using convolutional neural networks
论文作者
论文摘要
我们提出了一种深度机器学习算法，以从稀土磁性材料的热力学数据中提取晶体场（CF）史蒂文斯参数。该算法采用了二维卷积神经网络（CNN），该网络（CNN）受到磁化，磁敏感性和特定的热数据的训练，理论上在单离子近似中进行了计算，并使用标准小波转换进行了进一步处理。我们将方法应用于立方，六角形和四方对称性的晶体场，以及整数和半级数的总角动量值$ j $ j $ j $ j $ j $ j $ j $。我们评估了其在理论生成的合成和先前发布的实验数据上的性能，并在CEAGSB $ _2 $，pragsb $ _2 $和PRMG $ _2 $ _2 $ CU $ _9 $上评估其性能，并发现它可以可靠，准确地提取所有站点的CF参数以及所有J $ J $的CF参数。这表明CNN提供了一种无偏的方法来提取CF参数，从而避免了乏味的多参数拟合程序。
We present a deep machine learning algorithm to extract crystal field (CF) Stevens parameters from thermodynamic data of rare-earth magnetic materials. The algorithm employs a two-dimensional convolutional neural network (CNN) that is trained on magnetization, magnetic susceptibility and specific heat data that is calculated theoretically within the single-ion approximation and further processed using a standard wavelet transformation. We apply the method to crystal fields of cubic, hexagonal and tetragonal symmetry and for both integer and half-integer total angular momentum values $J$ of the ground state multiplet. We evaluate its performance on both theoretically generated synthetic and previously published experimental data on CeAgSb$_2$, PrAgSb$_2$ and PrMg$_2$Cu$_9$, and find that it can reliably and accurately extract the CF parameters for all site symmetries and values of $J$ considered. This demonstrates that CNNs provide an unbiased approach to extracting CF parameters that avoids tedious multi-parameter fitting procedures.
