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

Journal of Chemical Information and Modeling
Florbela PereiraJoao Aires-de-Sousa

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%).

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Citations

Aug 24, 2018·Journal of Cheminformatics·Florbela Pereira, João Aires-de-Sousa
Jul 2, 2018·The Journal of Chemical Physics·Peter Bjørn JørgensenMikkel N Schmidt
May 9, 2019·Advanced Science·Kunal GhoshPatrick Rinke
Jun 4, 2019·The Journal of Chemical Physics·Annika StukePatrick Rinke
Jan 16, 2021·The Journal of Chemical Physics·Chee-Kong LeeLiang Shi
Nov 12, 2019·Journal of Cheminformatics·Marta GlavatskikhBenoit Da Mota
Mar 7, 2021·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Jeffrey PlantePaul L A Popelier
Nov 20, 2020·Chemical Reviews·Julia Westermayr, Philipp Marquetand
Jan 5, 2021·Annual Review of Physical Chemistry·Tim J ZuehlsdorffChristine M Isborn
Apr 27, 2021·Discover Materials·Jose F RodriguesOsvaldo N Oliveira
Mar 13, 2020·Chemical Science·Jonathan A FineGaurav Chopra

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