Literature DB >> 17444695

Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach.

Hui Li1, LiLi Shi, Min Zhang, Zhongmin Su, XiuJun Wang, LiHong Hu, GuanHua Chen.   

Abstract

The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.

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Year:  2007        PMID: 17444695     DOI: 10.1063/1.2715579

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  6 in total

1.  Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.

Authors:  Lin Shen; Jingheng Wu; Weitao Yang
Journal:  J Chem Theory Comput       Date:  2016-09-06       Impact factor: 6.006

2.  Machine Learning for Electronically Excited States of Molecules.

Authors:  Julia Westermayr; Philipp Marquetand
Journal:  Chem Rev       Date:  2020-11-19       Impact factor: 60.622

3.  Assessment of the "6-31+G** + LANL2DZ" mixed basis set coupled with density functional theory methods and the effective core potential: prediction of heats of formation and ionization potentials for first-row-transition-metal complexes.

Authors:  Yue Yang; Michael N Weaver; Kenneth M Merz
Journal:  J Phys Chem A       Date:  2009-09-10       Impact factor: 2.781

4.  Improving the accuracy of Density Functional Theory (DFT) calculation for homolysis bond dissociation energies of Y-NO bond: generalized regression neural network based on grey relational analysis and principal component analysis.

Authors:  Hong Zhi Li; Wei Tao; Ting Gao; Hui Li; Ying Hua Lu; Zhong Min Su
Journal:  Int J Mol Sci       Date:  2011-04-01       Impact factor: 5.923

5.  A promising tool to achieve chemical accuracy for density functional theory calculations on Y-NO homolysis bond dissociation energies.

Authors:  Hong Zhi Li; Li Hong Hu; Wei Tao; Ting Gao; Hui Li; Ying Hua Lu; Zhong Min Su
Journal:  Int J Mol Sci       Date:  2012-06-28       Impact factor: 6.208

6.  Prediction Model of Organic Molecular Absorption Energies based on Deep Learning trained by Chaos-enhanced Accelerated Evolutionary algorithm.

Authors:  Mengshan Li; Suyun Lian; Fan Wang; Yanying Zhou; Bingsheng Chen; Lixin Guan; Yan Wu
Journal:  Sci Rep       Date:  2019-11-21       Impact factor: 4.379

  6 in total

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