论文标题
二元合金的光谱神经网络潜力
Spectral Neural Network Potentials for Binary Alloys
论文作者
论文摘要
在这项工作中，我们提出了一个数值实现，以根据原子邻居密度函数的谐波分析，计算Bartok等人（Phys。B，87，184115，2013）引入的原子中心描述符。具体而言，我们专注于两种类型的描述符，即平滑的SO（3）功率谱，并明确包含径向基础，以及通过将径向分量映射到四维超球的极角度来获得的（4）Bispectrum。使用这些描述符，基于线性和神经网络回归模型获得了二元Ni-MO合金的各种原子间电位。数值实验表明，两个描述符在准确性方面产生相似的结果。对于线性回归，当使用较大的带限制时，光滑的SO（3）功率谱优于SO（4）双光谱。在神经网络回归中，对于两个描述符的扩展组件的数量更少，可以实现更好的精度。因此，我们证明了光谱神经网络电位是大规模原子模拟的可行选择。
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.
