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EXCHANGE-CORRELATION POTENTIALS

机译:交换相关势

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摘要

We describe our implementation of the Zhao, Morrison, and Parr method [Phys. Rev. A 50, 2138 (1994)] for the calculation of molecular exchange-correlation potentials from high-level ab initio densities. The use of conventional Gaussian basis sets demands careful consideration of the value of the Lagrange multiplier associated with the constraint that reproduces the input density. Although formally infinite, we demonstrate that a finite value should be used in finite basis set calculations. The potential has been determined for Ne, HF, N-2, H2O, and N-2(1.5r(e)), and compared with popular analytic potentials. We have then examined bow well the Zhao, Morrison, Parr potential can be represented using a computational neural network. Assuming v(xc)=v(xc)(rho), we incorporate the neural network into a regular Kohn-Sham procedure [Phys. Rev. A 140, 1133 (1965)] with encouraging results. The extension of this method to include density derivatives is briefly outlined. (C) 1996 American Institute of Physics. [References: 36]
机译:我们描述了Zhao,Morrison和Parr方法的实现[Phys。 Rev. A 50,2138(1994)],用于从高水平的从头算密度计算分子交换相关电位。常规高斯基集的使用要求仔细考虑与再现输入密度的约束条件相关的拉格朗日乘数的值。尽管在形式上是无限的,但我们证明了在有限基集计算中应使用有限值。已确定了Ne,HF,N-2,H2O和N-2(1.5r(e))的电势,并与流行的分析电势进行了比较。然后,我们已经很好地检查了弓形,可以使用计算神经网络来表示Zhao,Morrison,Parr势。假设v(xc)= v(xc)(rho),我们将神经网络合并到常规的Kohn-Sham过程中。 Rev. A 140,1133(1965)],结果令人鼓舞。简要概述了此方法的扩展以包括密度导数。 (C)1996年美国物理研究所。 [参考:36]

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