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

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.

[1]  J. Pople,et al.  Self‐consistent molecular orbital methods. XX. A basis set for correlated wave functions , 1980 .

[2]  Parr,et al.  Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. , 1988, Physical review. B, Condensed matter.

[3]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[4]  Vera Kurková,et al.  Kolmogorov's theorem and multilayer neural networks , 1992, Neural Networks.

[5]  A. Becke.  Density-functional thermochemistry. III. The role of exact exchange , 1993 .

[6]  Gerrit Kateman,et al.  Using artificial neural networks for solving chemical problems Part I. Multi-layer feed-forward networks , 1994 .

[7]  Steven D. Brown,et al.  Neural network models of potential energy surfaces , 1995 .

[8]  M Karplus,et al.  Evolutionary optimization in quantitative structure-activity relationship: an application of genetic neural networks. , 1996, Journal of medicinal chemistry.

[9]  L. Curtiss,et al.  Assessment of Gaussian-3 and density functional theories for a larger experimental test set , 2000 .

[10]  M. Ratner.  Molecular electronic-structure theory , 2000 .

[11]  Trygve Helgaker,et al.  Molecular Electronic-Structure Theory: Helgaker/Molecular Electronic-Structure Theory , 2000 .

[12]  E Uriarte,et al.  Recent advances on the role of topological indices in drug discovery research. , 2001, Current medicinal chemistry.

[13]  A. Sekiya,et al.  Artificial neural network study for the estimation of the C–H bond dissociation enthalpies , 2002 .

[14]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[15]  Lihong Hu,et al.  Combined first-principles calculation and neural-network correction approach for heat of formation , 2003 .

[16]  Bahram Hemmateenejad,et al.  Application of ab initio theory to QSAR study of 1,4‐dihydropyridine‐based calcium channel blockers using GA‐MLR and PC‐GA‐ANN procedures , 2004, J. Comput. Chem..

[17]  Jan M. L. Martin,et al.  Development of density functionals for thermochemical kinetics. , 2004, The Journal of chemical physics.

[18]  GuanHua Chen,et al.  Improving the Accuracy of Density-Functional Theory Calculation: The Statistical Correction Approach , 2004 .

[19]  Marjan Vračko,et al.  Counter-propagation artificial neural network as a tool for the independent variable selection: Structure-mutagenicity study on aromatic amines , 2004, Molecular Diversity.

[20]  Stefan Grimme,et al.  Accurate description of van der Waals complexes by density functional theory including empirical corrections , 2004, J. Comput. Chem..

[21]  Jie Yang,et al.  Semiempirical Quantum Chemical Method and Artificial Neural Networks Applied for lambdamax Computation of Some Azo Dyes , 2004, J. Chem. Inf. Model..

[22]  GuanHua Chen,et al.  A Combined First-principles Calculation and Neural Networks Correction Approach for Evaluating Gibbs Energy of Formation , 2004 .

[23]  R Komanduri,et al.  Ab initio potential-energy surfaces for complex, multichannel systems using modified novelty sampling and feedforward neural networks. , 2005, The Journal of chemical physics.

[25]  GuanHua Chen,et al.  Neural network correction for heats of formation with a larger experimental training set and new descriptors , 2005 .

[26]  Ranga Komanduri,et al.  Molecular dynamics investigations of the dissociation of SiO2 on an ab initio potential energy surface obtained using neural network methods. , 2006, The Journal of chemical physics.

[27]  Jinwei Gao,et al.  Prediction of polyamide properties using quantum-chemical methods and BP artificial neural networks , 2006, Journal of molecular modeling.

[28]  Jinwei Gao,et al.  Calculation of polyamides melting point by quantum-chemical method and BP artificial neural networks , 2006, Journal of molecular modeling.

[29]  S. Grimme,et al.  Density functional theory with dispersion corrections for supramolecular structures, aggregates, and complexes of (bio)organic molecules. , 2007, Organic & biomolecular chemistry.

[30]  Min Zhang,et al.  Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach. , 2007, The Journal of chemical physics.

[31]  Roman M. Balabin,et al.  Comparison of linear and nonlinear calibration models based on near infrared (NIR) spectroscopy data for gasoline properties prediction , 2007 .

[32]  Roman M. Balabin,et al.  Capabilities of near Infrared Spectroscopy for the Determination of Petroleum Macromolecule Content in Aromatic Solutions , 2007 .

[33]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[34]  Xin Xu,et al.  The X1 method for accurate and efficient prediction of heats of formation. , 2007, The Journal of chemical physics.

[35]  M. Malshe,et al.  Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feedforward neural networks. II. Numerical application of the method. , 2007, The Journal of chemical physics.

[36]  Xin Xu,et al.  Improving the B3LYP bond energies by using the X1 method. , 2008, The Journal of chemical physics.

[37]  Roman M. Balabin,et al.  Polarization of Fluorescence of Asphaltene Containing Systems , 2008 .

[38]  S. Grimme,et al.  Theoretical thermodynamics for large molecules: walking the thin line between accuracy and computational cost. , 2008, Accounts of chemical research.

[39]  Xinliang Yu,et al.  DFT-based theoretical QSPR models of Q-e parameters for the prediction of reactivity in free-radical copolymerizations , 2008, Journal of molecular modeling.

[40]  Roman M. Balabin,et al.  Petroleum resins adsorption onto quartz sand: near infrared (NIR) spectroscopy study. , 2008, Journal of colloid and interface science.

[41]  Roman M. Balabin,et al.  Motor oil classification by base stock and viscosity based on near infrared (NIR) spectroscopy data , 2008 .

[42]  R. Bartlett,et al.  Monte Carlo configuration interaction predictions for the electronic spectra of Ne, CH2, C2, N2, and H2O compared to full configuration interaction calculations. , 2008, The Journal of chemical physics.

[43]  B. Yi,et al.  Prediction of the dielectric dissipation factor tanδ of polymers with an ANN model based on the DFT calculation , 2008 .

[44]  Bing Yi,et al.  Quantitative structure–property relationships for the reactivity parameters of acrylate monomers , 2008 .

[45]  G. A. Petersson,et al.  Uniformly convergent n-tuple-zeta augmented polarized (nZaP) basis sets for complete basis set extrapolations. I. Self-consistent field energies. , 2008, The Journal of chemical physics.

[46]  R Komanduri,et al.  Parametrization of analytic interatomic potential functions using neural networks. , 2008, The Journal of chemical physics.

[47]  Roman M. Balabin,et al.  Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra , 2008 .

[48]  L. Raff,et al.  Cis-->trans, trans-->cis isomerizations and N-O bond dissociation of nitrous acid (HONO) on an ab initio potential surface obtained by novelty sampling and feed-forward neural network fitting. , 2008, The Journal of chemical physics.

[49]  Roman M. Balabin.  Intermolecular dispersion interactions of normal alkanes with rare gas atoms: van der Waals complexes of n-pentane with helium, neon, and argon , 2008 .

[50]  Roman M. Balabin.  Enthalpy difference between conformations of normal alkanes: Raman spectroscopy study of n-pentane and n-butane. , 2009, The journal of physical chemistry. A.

[51]  A. Buciński,et al.  Artificial neural networks analysis used to evaluate the molecular interactions between selected drugs and human alpha1-acid glycoprotein. , 2009, Journal of pharmaceutical and biomedical analysis.

[52]  Roman M. Balabin.  Polar (acyclic) isomer of formic acid dimer: gas-phase Raman spectroscopy study and thermodynamic parameters. , 2009, The journal of physical chemistry. A.

[53]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .