Machine learning for the prediction of molecular dipole moments obtained by density functional theory

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Abstract

Machine learning (ML) algorithms were explored for the fast estimation of molecular dipole moments calculated by density functional theory (DFT) by B3LYP/6-31G(d,p) on the basis of molecular descriptors generated from DFT-optimized geometries and partial atomic charges obtained by empirical or ML schemes. A database was used with 10,071 structures, new molecular descriptors were designed and the models were validated with external test sets. Several ML algorithms were screened. Random forest regression models predicted an external test set of 3368 compounds achieving mean absolute error up to 0.44 D. The results represent a significant improvement of the dipole moments calculated using empirical point charges located at the nucleus, even assuming the DFT-optimized geometry (root mean square error, RMSE, of 0.68 D vs. 1.53 D and R2 = 0.87 vs. 0.66).[Figure not available: see fulltext.].

Original languageEnglish
Article number43
JournalJournal of Cheminformatics
Volume10
Issue number1
DOIs
Publication statusPublished - 1 Dec 2018

Keywords

  • Density functional theory (DFT)
  • Machine learning (ML)
  • Molecular dipole moment
  • Partial atomic charges
  • Quantitative structure property relationships (QSPR)

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