Improving B3LYP heats of formation with three-dimensional molecular descriptors

High-Accuracy Heats of Formation for Alkane Oxidation: From Small to Large via the Automated CBH-ANL Method

It is generally challenging to obtain high-accuracy predictions for the heat of formation for species with more than a handful of heavy atoms, such as those of importance in standard combustion mechanisms. To this end, we construct the CBH-ANL approach and illustrate that, for a set of 194 alkane oxidation species, it can be used to produce ΔH_f(0 K) values with 2σ uncertainties of 0.2-0.5 kcal mol^-1. This set includes the alkanes, hydroperoxides, and alkyl, peroxy, and hydroperoxyalkyl radicals for 17 representative hydrocarbon fuels containing up to 10 heavy atoms with various degrees of branching in the alkane backbone. The CBH-ANL approach, automated in the QTC and AutoMech software suites, builds balanced chemical equations for the calculation of ΔH_f(0 K), in which the reference species may be up to five heavy atoms. The high-level ANL0 and ANL1 reference ΔH_f(0 K) values are further refined for even the largest of these reference species with a novel laddering approach. We perform a comprehensive quantification of the uncertainties for both the individual reference species (the largest of which is 0.15 kcal mol^-1) and the propagation of those uncertainties when used in the calculation of ΔH_f(0 K) for the 194 target species. We examine the sensitivity of the predicted ΔH_f(0 K) values to (i) electronic energies from various methods, including ωB97X-D/cc-pVTZ, B2PLYP-D3/cc-pVTZ, CCSD(T)-F12b/cc-pVDZ-F12//B2PLYP-D3/cc-pVTZ, and CCSD(T)-F12b/cc-pVTZ-F12//B2PLYP-D3/cc-pVTZ; (ii) the zero-point vibrational energies (ZPVEs), where we consider harmonic ZPVEs as well as two scaling-based estimates of the anharmonic ZPVEs, all implemented for both ωB97X-D/cc-pVTZ and B2PLYP-D3/cc-pVTZ calculations; (iii) the particular CBH-ANL scheme employed; and (iv) the procedure for choosing the reference conformer for the analyses. The discussion concludes with a summary of the estimated overall uncertainty in the predictions and a validation of the predictions for the alkane subset.

Accurate heats of formation of polycyclic saturated hydrocarbons predicted by using the XYG3 type of doubly hybrid functionals

Polycyclic saturated hydrocarbons (PSHs) are attractive candidates as hydrocarbon propellants. To assess their potential values, one of the key factors is to determine their energy contents, such as to calculate their heats of formation (HOF). In this work, we have calculated HOFs for a set of 36 PSHs including exo-Tricyclo[5.2.1.0^(2,6) ] decane, the principal component of the high-energy density hydrocarbon fuel commonly identified as JP-10. The results from B3LYP, B3LYP-D3BJ, M06-2X, B2PLYP, B2PLYP-D3BJ, and the XYG3 type of doubly hybrid (xDH) functionals are presented. It is demonstrated here that the xDH functionals yield accurate HOFs in good agreement with those from experiments or the G4 theory. In particular, XYGJ-OS, a low scaling xDH functional, is shown to hold the promise for accurate prediction of HOFs for PSHs of larger sizes. © 2018 Wiley Periodicals, Inc.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.