标量机器学习中的张量量——基于单极模型的Born有效电荷
Scalar machine learning of tensorial quantities -- Born effective charges from monopole models
AI总结：
本研究提出基于单极模型的标量机器学习方法，用于学习Born有效电荷张量，通过导数捕捉其张量性质，并用于电荷分配和红外光谱计算。
摘要：
利用机器学习模型预测张量性质通常需要精心设计的张量描述符。在本工作中，我们提出了一种基于标量描述符的学习张量量的替代策略。我们将这种方法应用于Born有效电荷张量，证明通过利用Born有效电荷张量定义为极化对原子位移导数的方式，标量（单极）核模型可以成功捕捉其张量性质。我们比较了这种方法与张量（偶极）核模型，即在我们之前的工作中建立的模型，在其中Born有效电荷的张量结构直接编码在核中并通过其导数获得。这两种方法随后用于电荷分配，使单极和偶极贡献得以分离。最后，我们通过计算多种复杂材料的有限温度红外光谱来展示该框架的有效性。
英文摘要：
Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for learning tensorial quantities based on scalar descriptors. We apply this approach to the Born effective charge tensor, showing that scalar (monopole) kernel models can successfully capture its tensorial nature by exploiting the definition of the Born effective charge tensor as the derivative of the polarisation with respect to atomic displacements. We compare this method with tensorial (dipole) kernel models, as established in our previous work, in which the tensorial structure of the Born effective charge is encoded directly in the kernel and obtained via its derivative. Both approaches are then used for charge partitioning, enabling the separation of monopole and dipole contributions. Finally, we demonstrate the effectiveness of the framework by computing finite-temperature infrared spectra for a range of complex materials.
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