The selectivities in C-H oxidations of a variety of compounds by DMDO have been explored with density functional theory. There is a linear Evans-Polanyi-type correlation for saturated substrates. Activation energies correlate with reaction energies or, equivalently, BDEs (ΔH = 0.91*BDE - 67.8). Unsaturated compounds, such as alkenes, aromatics, and carbonyls, exhibit a different correlation for allylic and benzylic C-H bonds (ΔH = 0.35*BDE - 13.1). Bernasconi's Principle of Non-Perfect Synchronization (NPS) is found to operate here. The origins of this phenomenon were analyzed by a Distortion/Interaction model. Computations indicate early transition states for H-abstractions from allylic and benzylic C-H bonds, but later transition states for the saturated. The reactivities are mainly modulated by the distortion energy and the degree of dissociation of the C-H bond. While the increase in barrier with higher BDE is not unexpected from the Evans-Polanyi relationship, two separate correlations, one for saturated compounds, and one for unsaturated leading to delocalized radicals, were unexpected.
Density functional theory calculations (ωB97X-D) are reported for the reactions of methoxy, tert-butoxy, trichloroethoxy, and trifluoroethoxy radicals with a series of 26 C−H bonds in different environments characteristic of a variety of hydrocarbons and substituted derivatives. The variations in activation barriers are analyzed with modified Evans−Polanyi treatments to account for polarity and unsaturation effects. The treatments by Roberts and Steel and by Mayer have inspired the development of a simple treatment involving the thermodynamics of reactions, the difference between the reactant radical and product radical electronegativities, and the absence or presence of α-unsaturation. The three-parameter equation (ΔH ⧧ = 0.52ΔH rxn (1 − d) − 0.35Δχ AB 2 + 10.0, where d = 0.44 when there is α-unsaturation to the reacting C−H bond), correlates well with quantum mechanically computed barriers and shows the quantitative importance of the thermodynamics of reactions (dictated by the reactant and the product bond dissociation energies) and polar effects.
A well-performing machine learning (ML) model is obtained
by using
proper descriptors and artificial neural network (ANN) algorithms,
which can quickly and accurately predict activation free energy in
hydrogen atom transfer (HAT)-based sp
3
C–H activation.
Density functional theory calculations (UωB97X-D) are used to
establish the reaction system data sets of methoxyl (CH
3
O·), trifluoroethoxyl (CF
3
CH
2
O·),
tert
-butoxyl (tBuO·), and cumyloxyl (CumO·) radicals.
The simplified Roberts’ equation proposed in our recent study
works here [
R
2
= 0.84, mean absolute error
(MAE) = 0.85 kcal/mol]. Its performance is comparable with univariate
Mulliken-type electronegativity (χ) with the ANN model. The
ANN model with bond dissociation free energy, χ, α-unsaturation,
and Nolan buried volume (%
V
buried
) successively
improves
R
2
and MAE to 0.93 and 0.54 kcal/mol,
respectively. It reproduces the test sets of trichloroethoxyl (CCl
3
CH
2
O·) with
R
2
= 0.87 and MAE = 0.89 kcal/mol and accurately predicts the relative
experimental barrier of the HAT reactions with CumO· and the
site selectivity of CH
3
O·.
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