The performance of the rapid estimation of basis set error and correlation energy from partial charges method on new molecules of the G3/99 test set

2001 ◽  
Vol 106 (6) ◽  
pp. 404-411 ◽  
Author(s):  
Adrienn Ruzsinszky ◽  
G�bor I. Csonka ◽  
S�ndor Kristy�n
Keyword(s):  
Correlation Energy ◽  
Basis Set ◽  
Test Set ◽  
Rapid Estimation ◽  
Partial Charges

Machine-learned electron correlation model based on correlation energy density at complete basis set limit

2019 ◽  
Vol 151 (2) ◽  
pp. 024104 ◽  
Author(s):  
Takuro Nudejima ◽  
Yasuhiro Ikabata ◽  
Junji Seino ◽  
Takeshi Yoshikawa ◽  
Hiromi Nakai
Keyword(s):  
Energy Density ◽  
Electron Correlation ◽  
Correlation Energy ◽  
Basis Set ◽  
Correlation Model ◽  
Model Based ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Complete Basis Set Limit

scholarly journals Accurate global potential energy surface for the ground state of CH2+ by extrapolation to the complete basis set limit

RSC Advances ◽  
2018 ◽  
Vol 8 (25) ◽  
pp. 13635-13642 ◽  
Author(s):  
Lu Guo ◽  
Hongyu Ma ◽  
Lulu Zhang ◽  
Yuzhi Song ◽  
Yongqing Li
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Ground State ◽  
Energy Surface ◽  
Correlation Energy ◽  
Basis Set ◽  
Basis Sets ◽  
Complete Basis ◽  
Complete Basis Set ◽  
Complete Basis Set Limit

A full three-dimensional global potential energy surface is reported for the ground state of CH2+ by fitting accurate multireference configuration interaction energies calculated using aug-cc-pVQZ and aug-cc-pV5Z basis sets with extrapolation of the electron correlation energy to the complete basis set limit.

scholarly journals An Accurate and Efficient Method to Predict Y-NO Bond Homolysis Bond Dissociation Energies

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Hong Zhi Li ◽  
Lin Li ◽  
Zi Yan Zhong ◽  
Yi Han ◽  
LiHong Hu ◽  
...  
Keyword(s):  
Dft Calculations ◽  
Density Functional ◽  
Molecular Descriptors ◽  
Basis Set ◽  
Bond Dissociation Energies ◽  
Data Set ◽  
Test Set ◽  
Bond Dissociation ◽  
Dissociation Energies ◽  
Bond Homolysis

The paper suggests a new method that combines the Kennard and Stone algorithm (Kenstone, KS), hierarchical clustering (HC), and ant colony optimization (ACO)-based extreme learning machine (ELM) (KS-HC/ACO-ELM) with the density functional theory (DFT) B3LYP/6-31G(d) method to improve the accuracy of DFT calculations for the Y-NO homolysis bond dissociation energies (BDE). In this method, Kenstone divides the whole data set into two parts, the training set and the test set; HC and ACO are used to perform the cluster analysis on molecular descriptors; correlation analysis is applied for selecting the most correlated molecular descriptors in the classes, and ELM is the nonlinear model for establishing the relationship between DFT calculations and homolysis BDE experimental values. The results show that the standard deviation of homolysis BDE in the molecular test set is reduced from 4.03 kcal mol−1calculated by the DFT B3LYP/6-31G(d) method to 0.30, 0.28, 0.29, and 0.32 kcal mol−1by the KS-ELM, KS-HC-ELM, and KS-ACO-ELM methods and the artificial neural network (ANN) combined with KS-HC, respectively. This method predicts accurate values with much higher efficiency when compared to the larger basis set DFT calculation and may also achieve similarly accurate calculation results for larger molecules.

Sign in / Sign up

Export Citation Format

Share Document