Accurate Thermochemistry
of Hydrocarbon
Radicals via an Extended Generalized Bond Separation Reaction Scheme
- Publication date
- Publisher
Abstract
Detailed knowledge of hydrocarbon radical thermochemistry
is critical
for understanding diverse chemical phenomena, ranging from combustion
processes to organic reaction mechanisms. Unfortunately, experimental
thermochemical data for many radical species tend to have large errors
or are lacking entirely. Here we develop procedures for deriving high-quality
thermochemical data for hydrocarbon radicals by extending Wheeler
et al.’s “generalized bond separation reaction”
(GBSR) scheme (<i>J. Am. Chem. Soc</i>., <b>2009</b>, <i>131</i>, 2547). Moreover, we show that the existing
definition of hyperhomodesmotic reactions is flawed. This is because
transformation reactions, in which one molecule each from the predefined
sets of products and reactants can be converted to a different product
and reactant molecule, are currently allowed. This problem is corrected
via a refined definition of hyperhomodesmotic reactions in which there
are equal numbers of carbon–carbon bond types <i>inclusive</i> of carbon hybridization and number of hydrogens attached. Ab initio
and density functional theory (DFT) computations using the expanded
GBSRs are applied to a newly derived test set of 27 hydrocarbon radicals
(HCR27). Greatly reduced errors in computed reaction enthalpies are
seen for hyperhomodesmotic and other highly balanced reactions classes,
which benefit from increased matching of hybridization and bonding
requirements. The best performing DFT methods for hyperhomodesmotic
reactions, M06-2X and B97-dDsC, give average deviations from benchmark
computations of only 0.31 and 0.44 (±0.90 and ±1.56 at the
95% confidence level) kcal/mol, respectively, over the test set. By
exploiting the high degree of error cancellation provided by hyperhomodesmotic
reactions, accurate thermochemical data for hydrocarbon radicals (e.g.,
enthalpies of formation) can be computed using relatively inexpensive
computational methods