二元合金的谱神经网络势
Spectral Neural Network Potentials for Binary Alloys
摘要：
在这项工作中，我们提出了一个数值实现来计算Bartok等人（Phys. Rev. B，87，184 - 115，2013）介绍的原子中心描述符的基础上的原子邻居密度函数的谐波分析。具体来说，我们专注于两种类型的描述符，光滑SO（3）功率谱与径向基显式包括和SO（4）双谱通过映射到一个极角的径向分量的四维超球。利用这些描述符，基于线性和神经网络回归模型，得到了二元Ni-Mo合金的各种原子间相互作用势。数值实验表明，这两个描述符产生类似的结果的准确性。对于线性回归，当使用大的带限时，光滑SO（3）功率谱优于SO（4）双谱。在神经网络回归中，对于两个描述符，可以用甚至更少数量的扩展分量来实现更好的精度。因此，我们证明了谱神经网络势是大规模原子模拟的可行选择。
英文摘要：
In this work, we present a numerical implementation to compute the atom centered descriptors introduced by Bartok et al (Phys. Rev. B, 87, 184115, 2013) based on the harmonic analysis of the atomic neighbor density function. Specifically, we focus on two types of descriptors, the smooth SO(3) power spectrum with the explicit inclusion of a radial basis and the SO(4) bispectrum obtained through mapping the radial component onto a polar angle of a four dimensional hypersphere. With these descriptors, various interatomic potentials for binary Ni-Mo alloys are obtained based on linear and neural network regression models. Numerical experiments suggest that both descriptors produce similar results in terms of accuracy. For linear regression, the smooth SO(3) power spectrum is superior to the SO(4) bispectrum when a large band limit is used. In neural network regression, a better accuracy can be achieved with even less number of expansion components for both descriptors. As such, we demonstrate that spectral neural network potentials are feasible choices for large scale atomistic simulation.
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