利用卷积神经网络学习晶体场参数
Learning crystal field parameters using convolutional neural networks
摘要：
本文提出了一种深度机器学习算法，用于从稀土磁性材料的热力学数据中提取晶场（CF）史蒂文斯参数。该算法采用了一个二维卷积神经网络（CNN），该网络在磁化强度、磁化率和比热数据上进行训练，这些数据是在单离子近似下理论计算的，并使用标准小波变换进行进一步处理。我们将该方法应用于晶体领域的立方，六方和tetraquartz对称性和整数和半整数总角动量值$J$的基态多重。我们评估其性能的理论上产生的合成和以前发表的实验数据CeAgSb $_2$，PrAgSb $_2$和PrMg $_2$ Cu $_9$，并发现它可以可靠和准确地提取CF参数的所有网站的对称性和值的$J$考虑。这表明CNN提供了一种无偏的方法来提取CF参数，避免了繁琐的多参数拟合过程。
英文摘要：
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.
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