论文标题
适当的正交描述符，以提高原子质电位
Proper Orthogonal Descriptors for Efficient and Accurate Interatomic Potentials
论文作者
论文摘要
我们介绍了适当的正交描述符，以有效而准确地表示势能表面。势能表面表示为参数势的多体扩展，其中电势是原子位置和参数的函数。 Karhunen-Loève（KL）扩展用于将参数化电位分解为一组正确的正交描述符（PODS）。由于KL扩展的迅速收敛，可以详尽地对相关快照进行采样，以准确地代表原子能环境，并使用少量的描述符。适当的正交描述符用于使用描述符的线性扩展并确定来自加权最小二乘回归的扩展系数，以针对密度功能理论（DFT）训练集确定扩展系数。我们对先前发布的DFT数据集的POD电位进行了全面评估，其中包括LI，MO，CU，NI，SI，GE，GE和TA元素。数据集代表了多种金属，过渡金属和半导体池。 POD电位的准确性与最先进的机器学习潜力（例如光谱邻域分析电位（SNAP）和原子簇扩展（ACE））相当。
We present the proper orthogonal descriptors for efficient and accuracy representation of the potential energy surface. The potential energy surface is represented as a many-body expansion of parametrized potentials in which the potentials are functions of atom positions and parameters. The Karhunen-Loève (KL) expansion is employed to decompose the parametrized potentials into a set of proper orthogonal descriptors (PODs). Because of the rapid convergence of the KL expansion, relevant snapshots can be sampled exhaustively to represent the atomic neighborhood environment accurately with a small number of descriptors. The proper orthogonal descriptors are used to develop interatomic potentials by using a linear expansion of the descriptors and determining the expansion coefficients from a weighted least-squares regression against a density functional theory (DFT) training set. We present a comprehensive evaluation of the POD potentials on previously published DFT data sets comprising Li, Mo, Cu, Ni, Si, Ge, and Ta elements. The data sets represent a diverse pool of metals, transition metals, and semiconductors. The accuracy of the POD potentials are comparable to that of state-of-the-art machine learning potentials such as the spectral neighbor analysis potential (SNAP) and the atomic cluster expansion (ACE).
