DOI: 10.1063/1.3206326. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

10.1063/1.3206326

Crossref journal-article

AIP Publishing

The Journal of Chemical Physics (317)

Abstract

Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree–Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6±0.2 kcal mol−1. In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

Bibliography

Balabin, R. M., & Lomakina, E. I. (2009). Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies. The Journal of Chemical Physics, 131(7).

Authors 2

Roman M. Balabin (first)
Ekaterina I. Lomakina (additional)

References 57 Referenced 139

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See EPAPS supplementary material at http://dx.doi.org/10.1063/1.3206326 for molecular set used for DFT+ANN study, all DFT (HF, BLYP, B3LYP, BMK) energies with 6-311G, 6-311G(d), 6-311G(d,p), 6-311G(2df,2pd), and 6-311G(3df,3pd) BSs, and ready-to-use MATLAB.mat file with best ANN parameters, presented in table format.

Dates

Type	When
Created	16 years ago (Aug. 18, 2009, 6:23 p.m.)
Deposited	2 years, 2 months ago (June 26, 2023, 12:19 a.m.)
Indexed	1 week, 5 days ago (Aug. 22, 2025, 12:49 a.m.)
Issued	16 years ago (Aug. 17, 2009)
Published	16 years ago (Aug. 17, 2009)
Published Online	16 years ago (Aug. 17, 2009)
Published Print	16 years ago (Aug. 21, 2009)

Funders 0

None

BibTeX

@article{Balabin_2009, title={Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies}, volume={131}, ISSN={1089-7690}, url={http://dx.doi.org/10.1063/1.3206326}, DOI={10.1063/1.3206326}, number={7}, journal={The Journal of Chemical Physics}, publisher={AIP Publishing}, author={Balabin, Roman M. and Lomakina, Ekaterina I.}, year={2009}, month=aug }

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