%0 Journal Article %1 Hu2003Combined %A Hu, LiHong %A Wang, XiuJun %A Wong, LaiHo %A Chen, GuanHua %D 2003 %K neural %T Combined first&\#45;&\#45;principles calculation and neural&\#45;&\#45;network correction approach as a powerful tool in computational physics and chemistry %U http://arxiv.org/abs/physics/0306075 %X Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation \$\DelH\$ and standard Gibbs energy of formation \$\DelG\$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated \$\DelH\$ (\$\DelG\$) for the 180 molecules is reduced from 21.4 (22.3) kcal\$\cdotp\$mol\$^-1\$ to 3.1 (3.3) kcal\$\cdotp\$mol\$^-1\$ for B3LYP/6-311+G(d,p) and from 12.0 (12.9) kcal\$\cdotp\$mol\$^-1\$ to 3.3 (3.4) kcal\$\cdotp\$mol\$^-1\$ for B3LYP/6-311+G(3df,2p) before and after the neural-network correction.

@article{Hu2003Combined, abstract = {Despite of their success, the results of first-principles quantum mechanical calculations contain inherent numerical errors caused by various approximations. We propose here a neural-network algorithm to greatly reduce these inherent errors. As a demonstration, this combined quantum mechanical calculation and neural-network correction approach is applied to the evaluation of standard heat of formation \$\DelH\$ and standard Gibbs energy of formation \$\DelG\$ for 180 organic molecules at 298 K. A dramatic reduction of numerical errors is clearly shown with systematic deviations being eliminated. For examples, the root--mean--square deviation of the calculated \$\DelH\$ (\$\DelG\$) for the 180 molecules is reduced from 21.4 (22.3) kcal\$\cdotp\$mol\$^{-1}\$ to 3.1 (3.3) kcal\$\cdotp\$mol\$^{-1}\$ for B3LYP/6-311+G({\it d,p}) and from 12.0 (12.9) kcal\$\cdotp\$mol\$^{-1}\$ to 3.3 (3.4) kcal\$\cdotp\$mol\$^{-1}\$ for B3LYP/6-311+G(3{\it df},2{\it p}) before and after the neural-network correction.}, added-at = {2019-02-23T22:09:48.000+0100}, archiveprefix = {arXiv}, author = {Hu, LiHong and Wang, XiuJun and Wong, LaiHo and Chen, GuanHua}, biburl = {https://www.bibsonomy.org/bibtex/28e20c2c05d044897469eac391492dcfd/cmcneile}, citeulike-article-id = {7132486}, citeulike-linkout-0 = {http://arxiv.org/abs/physics/0306075}, citeulike-linkout-1 = {http://arxiv.org/pdf/physics/0306075}, day = 10, eprint = {physics/0306075}, interhash = {65c2a039878ddf35e717aa7aaf127630}, intrahash = {8e20c2c05d044897469eac391492dcfd}, keywords = {neural}, month = jun, posted-at = {2010-05-06 16:33:36}, priority = {2}, timestamp = {2019-02-23T22:15:27.000+0100}, title = {{Combined first\&\#45;\&\#45;principles calculation and neural\&\#45;\&\#45;network correction approach as a powerful tool in computational physics and chemistry}}, url = {http://arxiv.org/abs/physics/0306075}, year = 2003 }