Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals

Florbela Pereira, Kaixia Xiao, Diogo A.R.S. Latino, Chengcheng Wu, Qingyou Zhang, Joao Aires-De-Sousa

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).

Original languageEnglish
Pages (from-to)11-21
Number of pages11
JournalJournal of Chemical Information and Modeling
Volume57
Issue number1
DOIs
Publication statusPublished - 23 Jan 2017

Keywords

  • ORGANIC PHOTOVOLTAICS
  • POLYMER DIELECTRICS
  • EXPERIMENTAL-MODELS
  • BIG DATA
  • ELECTROPHILICITY
  • NUCLEOPHILICITY

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