改进了系数标度全相关和多系数相关方法

PhysChemComm Pub Date : 1999-01-01 DOI:10.1039/A908207G
Christine M. Tratz, Patton L. Fast, D. Truhlar
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引用次数: 27

摘要

我们重新优化了10种标度全相关(SAC)方法、5种经验无限基(EIB)方法和18种多系数相关(MC)方法的系数,包括多系数SAC和多系数高斯-2和高斯-3的特殊情况。新的参数化基于一个包含82个原子化能的训练集,除了多系数高斯-2,它被限制在H和第一周期(核电荷数(9)),并且基于一个包含52个原子化能的训练集。每种方法都可以使用或不包括核心相关效应,这是基于在125个分子训练集上优化的一组新参数。采用新参数后,82个分子的原子化能平均无符号误差平均降低20%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved coefficients for the scaling all correlation and multi-coefficient correlation methods
We have re-optimized the coefficients for ten scaling all correlation (SAC) methods, five empirical infinite basis (EIB) methods, and 18 multi-coefficient (MC) correlation methods, including the special cases of multi-coefficient SAC and multi-coefficient Gaussian-2 and Gaussian-3. The new parameterization is based on a training set of 82 atomization energies except for multi-coefficient Gaussian-2, which is restricted to H and the first period (nuclear charge ( 9) and is based on a training set of 52 atomization energies. Each method may be employed with or without including core-correlation effects, which are based on a new set of parameters optimized on a 125-molecule training set. The mean unsigned error in the atomization energies of the 82-molecule set is reduced on average by 20% when the new parameters used here are adopted.
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