Abstract
In this study, the performance of 17 different density functional theory functionals was compared for the calculation of the bond dissociation energy (BDE) values of X−H (X=C, N, O, S) bonds of aromatic compounds. The effect of the size of the basis set (expansions of 6-31(G)) was also assessed for the initial geometry and zero-point energy calculations, followed by the single-point BDE calculations with different model chemistries with the 6-311 + (3df,2p) basis set. It was found that the size of the basis set for geometry optimization has a much smaller effect on the accuracy of BDE than the choice of functional for the following single-point calculations. The M06-2X, M05-2X and M08−HX functionals yielded highly accurate BDE values compared to experimental data (with the average mean unsigned error MUE = 1.2–1.5 kcal mol−1), performing better than any of the other functionals. The results suggest that geometry optimization may be performed with B3LYP functional and a small basis set, whereas the M06-2X, M05-2X and M08-HX density functionals with a suitably large basis set offer the best method for calculating BDEs of ArX−H (X=C, N, O, S) bonds.
Keywords: DFT, functional, bond dissociation energy, aromatic compounds, M06-2X, M08-HX
1. Introduction
The Kohn–Sham density functional theory (DFT) offers one of the most widely used methods in computational and theoretical chemistry, with significant progress achieved in developing and validating highly accurate exchange and correlation functionals during the last decade [1–4]. The broad range of model chemistries offers various strengths and weaknesses, where higher accuracy usually means increasingly, often prohibitively high cost in terms of CPU time. As the development effort reached the final frontiers within the limitations of the method, DFT has increasingly turned into a routine tool for practical applications [5–8]. With a parallel increase in desktop computing power, the DFT method now advanced from the realm of specialists to become a mainstream, routine method for any chemist seeking fast validation for reaction pathways and energetics or predictions of various physico-chemical properties of compounds. That necessitates moving away from the ‘higher is better’ attitude towards the level of theory and a kind of cost-benefit analysis: for practical purposes, what level of theory is sufficient to address a particular problem [9].
Bond dissociation energy (BDE) is defined as the enthalpy change of the homolytic cleavage of a specific bond, providing one of the essential characteristics of the breakdown reactivities of compounds in the context of e.g. their antioxidant activity [10,11]. Experimental methods to determine BDE are limited to measuring energy flows, such as calorimetry, and rely on breaking a known weak bond. Thus, experimental measurements of BDE are limited to simple and/or small molecules, hence they are unsuitable in many real-world situations where large and complicated molecules are involved, which is often the case with natural products. The recent advancement of first-principle-based computational chemistry techniques has provided an alternate method for obtaining precise BDE for any molecule of interest [12]. However, only at highly connected post Hartree–Fock levels of theory have ab initio approaches proven accurate among the known computational chemistry methods. The G2 and CCSD(T) approaches, for example, have been shown to be accurate but only for tiny compounds [13–19]. It is not feasible to perform all such calculations on supercomputers, and the above-stated advantages of DFT justify the less accurate but more practical method, especially for predicting the characteristics of very large molecules [5,10,11,19,20].
Seventeen density functionals stand out with their performance in calculating thermochemical parameters, listed in the electronic supplementary material, table S1, and thus used frequently for addressing such problems [1,21]. In particular, M05-2X, M05 and MPWB1 K are recommended for general thermochemistry, kinetics and non-covalent interactions involving nonmetals [1]. BB1 K, MPWB1 K, BMK and M05-2X gave the best result for barrier heights in the HTBH38/04 database [1]. M05-2X, B1B95, MPW1B95, BMK, M05, B98 and B97-2 gave a good performance for reaction energies in the HTBH38/04 database [1]. M05-2X, BMK, BB1 K, MPWB1 K and M05 were the best for kinetics in the HTBH38/04 database [1]. LC−ωPBE, M06-2X, BMK, B2PLYP, M05-2X and MN12SX yielded high-quality results in the calculation of rate constants for radical-molecule reactions in an aqueous solution [1]. M06 yields good results for transition-metal chemistry and other multi-reference cases [21]. The M08-HX functional performs better than M06-2X, M05-2X and B3LYP for a range of problems, including main-group thermochemistry, kinetics, non-covalent interactions and electronic spectroscopy [22].
Many previous studies demonstrated that the radical scavenging activity of aromatic compounds such as phenolics [23–29], aromatic amines [30–32] or thiols [33,34], particularly in lipid media, follows the hydrogen transfer mechanism characterized by BDE(X–H, X=C, N, O, S) values. While several studies addressed the optimization of model chemistries for calculating specific physico-chemical parameters of specific compound families [35–45], these works also highlight that the performance of model chemistries cannot be evaluated in the absolute and has to be repeated for each practical problem sufficiently different from the ones addressed before. One such problem is the computation of BDE(X–H, X=C, N, O, S) of compounds containing aromatic rings. Thus, in this study, the 17 DFT functionals identified above have been evaluated for computing BDE values of the X−H (X=C, N, O, S) bond of ArX−H compounds to find the most convenient method for computing this thermochemical parameter for this specific case.
2. Computational details
The Gaussian 16 package of programs was used for all calculations in our work. BDE calculations were performed according to the protocol of Wright [11] that is widely used in the literature [10,18–20,46]. In this study, the geometry is optimized, and zero-point energy corrections are calculated with the same model chemistry for all compounds and for all of the following by single-point calculations of BDE values with a range of model chemistries. The calculated BDEs were compared with the experimental BDE values obtained from the literature [47].
BDE is the total energy required for homolytic breakage of a specific bond, here the X−H of the ArX−H compound. BDE is thus the enthalpy change of the reaction
| 2.1 |
expressed numerically as
| 2.2 |
For the compounds that have multiple conformers, all of these were screened by the Spartan Software with the MMFF/molecular mechanics module [48] and the conformer with the lowest electronic energy was included in the analysis. ArX−H (X=C, N, O, S) substances were chosen as model compounds for performance testing of the 17 density functionals on the condition that experimental values of BDEs exist for them.
3. Results and discussion
3.1. Effect of the size of the basis set
Previous studies showed that the B3LYP functional is sufficient and accurate for geometry optimization and frequency calculations [20,21,49–52]. Thus, calculations were performed with B3LYP functional and five typical basis sets including 6-31G(d), 6-31 + G(d), 6-31 + G(d,p), 6-311G(d,p) and 6-311 ++ G(d,p)) for the geometry optimization and frequency calculations, followed by single-point calculations of BDEs using 17 different functionals and the 6-311 + G(3df,2p) level [21]. This is a protocol intended for modest computational resources, where the larger basis set is only used where it is necessary; the aim here is to validate this approach against experimental data.
In the first section, we assess the variation affected by the size of the basis sets by cross-comparison of the data. Arguably any improvement in the numerical values should manifest as a difference between the values calculated with different basis sets, and therefore the analysis does not require referencing experimental data to reveal the ideal size of the basis set. The detailed data including differences in numerical values between basis sets in comparison to the smallest 6-31G(d) level are presented in the electronic supplementary material, table S3–S6 for C−H, S8, S10 and S12 for N−H, O−H and S−H bonds, respectively. Given the very small variation observed for C−H bonds, for N−H, O−H and S−H bonds only the smallest and largest studied basis sets were compared.
As per these data, variations of the BDE values based on geometry optimization with different size basis sets are mostly lower than 0.3 kcal mol−1 (corresponding to 0.3%). The only exception was observed for BDE calculated with B2PLYP functional at the N−H bonds (electronic supplementary material, table S8), and the S−H bond of compounds SH1 and SH3 (electronic supplementary material, table S12). The larger error here is likely related to the chemical environment of these bonds that requires more accurate geometry optimization and is beyond the scope of this work to analyse in detail. Overall it was found that the geometry optimization with larger basis sets does not yield significant differences compared to smaller basis sets; hence even without comparison to experimental data, it is clear that they cannot deliver much improvement of accuracy (range from 0.1% to 0.3%). Importantly the data does not suggest, per se, that the size of the basis set in geometry optimization is irrelevant for BDE calculations, rather than the larger basis set is not expected to deliver substantial improvement, conversely for very large molecules reducing the size of the basis set is a viable compromise to obtain sensible predictions of BDE. As the few specific cases mentioned above imply, for higher accuracy, it is still desirable to perform calculations with a larger basis set. Therefore, in the next section, we focus our analysis on the geometry optimized with the smallest basis set, i.e. the DFT/6-311 + G(3df,2p)//B3LYP/6-31G(d) chemistry model, to evaluate the accuracy of different functionals in the calculation of BDE values of the studied compounds against experimental data.
3.2. The bond dissociation energy of C−H
In this part, the performance of 17 popular functionals was examined for computing the BDE values of 11 ArC−H substances, where hydrogen atoms are either directly bound to the aromatic ring or bound to a carbon atom that is a substituent on the aromatic ring. The compounds were benzene (CH1) [53], toluene (CH2) [54], ethylbenzene (CH3) [55], diphenylmethane (CH4) [56], indene (CH5) [57], tetralin (CH6) [58], 9-10-dihydroanthracene (CH8) [59], xanthene (CH9) [60], fluorine (CH10) [61] and 9-anthracenylmethane (CH11) [56]. All of these substances have accurate experimentally defined BDE values in the literature (electronic supplementary material, table S2) [47] that were used as a reference to assess the performance of the functionals.
As shown in table 1, the best-performing functionals for calculating BDE values of C−H substances are M08-HX, M06-2X, BMK and M05-2X, in that order. Remarkably, the mean unsigned errors (MUE) of M06-2X and BMK are equal to M08-HX (1.3 kcal mol−1). The maximum absolute error (MaxAE) of M08-HX has its minimum at 3.0 kcal mol−1. The MaxAE values of BMK and M05-2X are higher than those of the M08-HX and M06HX by about 1.0−1.6 kcal mol−1. A similar trend was observed at absolute values; the relative MUE of the four listed functionals above are not significantly different (range from 1.4% to 1.6%), whereas the relative MaxAE of BMK and M05-2X (4.8% and 5.5%, respectively) are much higher than that of M08-HX and M06-2X (3.9% and 3.8%, respectively). The rest of the tested functionals for the C−H bond BDE calculations also have the MaxAE lower than 10.0 kcal mol−1 except B2PLYP (12.0 kcal mol−1).
Table 1.
Absolute (in kcal mol−1)a and relative (in %) deviation of C−H BDEs from experimental values.
| C−H BDE | CH1 | CH2 | CH3 | CH4 | CH5 | CH6 | CH7 | CH8 | CH9 | CH 10 | CH 11 | absolute values |
relative valuesc |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MUEb | MaxAEb | MUEb | MaxAEb | ||||||||||||
| M06-2X | −1.9 | 1.3 | 1.6 | −1.4 | −3.2 | 0.6 | −0.7 | 2.5 | 0.2 | 0.0 | −0.8 | 1.3 | 3.2 | 1.5 | 3.8 |
| M05-2X | −0.4 | −0.1 | 0.6 | −2.4 | −4.6 | −0.5 | 0.9 | 1.3 | −0.8 | −1.1 | −2.3 | 1.4 | 4.6 | 1.6 | 5.5 |
| M06 | −3.7 | −1.4 | −1.6 | −5.2 | −6.2 | −2.9 | −2.3 | −1.6 | −3.4 | −3.5 | −3.7 | 3.2 | 6.2 | 3.7 | 7.4 |
| M05 | −4.0 | −3.1 | −3.2 | −7.0 | −8.0 | −4.8 | −2.9 | −3.2 | −4.5 | −5.0 | −5.8 | 4.7 | 8.0 | 5.3 | 9.7 |
| BMK | −1.0 | 0.5 | 0.9 | −2.9 | −4.0 | −0.3 | 0.1 | 0.9 | −1.3 | −1.0 | −1.7 | 1.3 | 4.0 | 1.5 | 4.8 |
| MPW1B95 | −1.7 | −0.4 | −0.6 | −4.6 | −5.5 | −1.5 | −0.6 | −0.4 | −3.0 | −2.7 | −2.6 | 2.2 | 5.5 | 2.5 | 6.7 |
| B1B95 | −2.4 | −0.9 | −1.2 | −5.2 | −6.1 | −2.1 | −1.3 | −1.1 | −3.7 | −3.3 | −3.1 | 2.8 | 6.1 | 3.1 | 7.4 |
| B98 | −2.7 | −2.1 | −1.8 | −6.0 | −6.7 | −3.1 | −1.8 | −2.3 | −4.4 | −4.0 | −4.0 | 3.5 | 6.7 | 4.0 | 8.1 |
| B97-2 | −3.1 | −2.4 | −2.4 | −6.5 | −7.5 | −3.9 | −2.1 | −3.0 | −5.2 | −4.8 | −4.6 | 4.1 | 7.5 | 4.7 | 9.1 |
| LC-ωPBE | −4.3 | −2.2 | −1.9 | −4.0 | −5.3 | −3.2 | −3.2 | −0.4 | −2.1 | −2.0 | −6.3 | 3.2 | 6.3 | 3.6 | 7.5 |
| B3LYP | −2.4 | −2.1 | −1.9 | −6.1 | −6.7 | −3.3 | −1.5 | −2.5 | −4.5 | −4.1 | −4.1 | 3.6 | 6.7 | 4.1 | 8.1 |
| cam-B3LYP | −1.4 | −0.8 | −0.4 | −3.7 | −4.8 | −1.8 | −0.4 | 0.0 | −1.9 | −1.6 | −3.5 | 1.8 | 4.8 | 2.1 | 5.7 |
| B2PLYP | −8.9 | −8.9 | −7.9 | −11.4 | −11.9 | −9.6 | −8.0 | −8.0 | −9.1 | −9.1 | −12.0 | 9.5 | 12.0 | 10.8 | 14.3 |
| MPWB1 K | −1.3 | −0.1 | −0.1 | −3.7 | −4.9 | −1.1 | −0.1 | 0.4 | −1.9 | −1.8 | −2.4 | 1.6 | 4.9 | 1.8 | 5.9 |
| BB1 K | −1.7 | −0.4 | −0.5 | −4.2 | −5.3 | −1.5 | −0.6 | −0.1 | −2.4 | −2.2 | −2.7 | 2.0 | 5.3 | 2.2 | 6.4 |
| BB95 | −4.0 | −2.7 | −3.3 | −8.3 | −8.3 | −4.2 | −3.1 | −4.1 | −7.2 | −6.3 | −5.1 | 5.1 | 8.3 | 5.9 | 9.9 |
| M08-HX | −0.3 | 1.6 | 1.7 | −1.1 | −2.7 | 1.1 | 1.0 | 3.0 | 0.5 | 0.5 | −0.4 | 1.3 | 3.0 | 1.4 | 3.9 |
aAll tested substance geometries were pre-optimized with B3LYP/6-31G(d) functional, then were calculated single-point energy using 6-311 + G(3df,2p) basis set.
bMUE (mean unsigned error) = mean absolute deviation; MaxAE = max absolute error.
cThe relative values of MUEs were calculated from it, and the average of BDE values of all C−H substances; the relative values of MaxAE were calculated from it and its corresponding value for BDE of C−H substances.
These results correlate well with reports on comparing the accuracy of various functionals for energetic calculations on specific classes of compounds. Yan Zhao et al. [21] tested nine functionals against three experimental databases of Izgorodina et al. [62], and found that M06-2X and M05-2X showed excellent performance for calculating energetics involving radicals as well as the alkyl oxygen BDEs of nitroxides. Similar results were also reported in other studies [38–42], once again asserting the superior performance of M06-2X and M05-2X over any other density functionals.
3.3. The bond dissociation energy of N−H
Nine ArN−H substances, which have the hydrogen atom directly bound to the aromatic ring or linked to the aromatic ring by a substituent nitrogen atom, were used to evaluate computing methods for N−H bond dissociation energies. The compounds were 4-flouroaniline (NH1) [63], N-methyl-N-phenylamine (NH2) [64], phenylhydrazine (NH3) [65], diphenylamine (NH4) [66], 1,3-oxazolidin-2-one (NH5) [67], 2-piperidone (NH6) [68], 2,2,4-trimethyl-1,2,3,4-tetrahydroquinoline (NH7) [69], carbazole (NH8) [70] and dimethyl-phenothioazine (NH9) [71]. The experimental BDE values were collected from the literature (electronic supplementary material, table S7) [47].
The calculated results (table 2) show that M05-2X and M06-2X are again the best performers with minor deviations, MUEs being 1.0 kcal mol−1 and 1.2 kcal mol−1, respectively. Their MaxAEs are minor, with 2.6 kcal mol−1 for M05-2X and 2.9 kcal mol−1 for M06-2X. These values are lower than in the case of the C−H bond (4.6 kcal mol−1 for M05-2X and 3.2 kcal mol−1 for M06-2X). The M08-HX and MPWB1 K functionals have an equal error of 1.8 kcal mol−1 of the absolute MUE, whereas the relative MUEs of M05-2X, M06-2X, M08-HX and MPWB1 K were 1.1% to 2.0%. The BMK functional yields a good MUE with 1.9 kcal mol−1, and MaxAE is 4.0 kcal mol−1; however, the MUEs of the rest of the density functionals range from 2.1 kcal mol−1 to 6.1 kcal mol−1.
Table 2.
Absolute (in kcal mol−1)a and relative (in %) deviation of N−H BDEs from experimental values.
| N-H BDE | NH1 | NH2 | NH3 | NH4 | NH5 | NH6 | NH7 | NH8 | NH9 | absolute values |
relative valuesc |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MUEb | MaxAEb | MUEb | MaxAEb | ||||||||||
| M06-2X | 2.6 | 1.0 | 2.6 | 0.1 | −0.4 | −2.9 | 1.1 | −0.2 | 0.1 | 1.2 | 2.9 | 1.4 | 2.6 |
| M05-2X | 1.9 | 0.1 | 1.9 | −0.8 | −0.1 | −2.6 | 0.2 | −0.8 | −0.3 | 1.0 | 2.6 | 1.1 | 2.4 |
| M06 | −0.8 | −2.5 | 0.2 | −3.8 | −2.5 | −4.9 | −2.4 | −4.6 | −4.1 | 2.9 | 4.9 | 3.2 | 4.5 |
| M05 | −2.3 | −4.0 | −1.4 | −5.1 | −3.6 | −5.7 | −4.6 | −5.6 | −5.1 | 4.1 | 5.7 | 4.6 | 5.2 |
| BMK | 0.5 | −1.3 | 0.6 | −2.4 | −1.8 | −4.0 | −1.7 | −2.5 | −2.1 | 1.9 | 4.0 | 2.1 | 3.7 |
| MPW1B95 | 0.2 | −1.8 | −0.3 | −3.1 | −2.5 | −4.7 | −1.9 | −3.3 | −3.0 | 2.3 | 4.7 | 2.5 | 4.3 |
| B1B95 | −0.5 | −2.4 | −1.1 | −3.8 | −3.3 | −5.5 | −2.7 | −4.0 | −3.9 | 3.0 | 5.5 | 3.3 | 5.0 |
| B98 | −2.0 | −3.9 | −1.6 | −5.5 | −4.9 | −6.9 | −4.4 | −6.1 | −5.4 | 4.5 | 6.9 | 5.0 | 6.3 |
| B97-2 | −2.2 | −4.2 | −2.4 | −5.8 | −5.1 | −7.1 | −4.7 | −6.2 | −6.3 | 4.9 | 7.1 | 5.4 | 6.5 |
| LC-wPBE | −1.0 | −2.8 | −0.3 | −2.5 | −3.0 | −5.4 | −3.0 | −2.4 | −1.4 | 2.4 | 5.4 | 2.7 | 5.0 |
| B3LYP | −1.8 | −3.8 | −1.6 | −5.3 | −4.7 | −6.7 | −4.3 | −5.9 | −5.4 | 4.4 | 6.7 | 4.9 | 6.1 |
| cam-B3LYP | −0.2 | −2.0 | 0.4 | −2.7 | −2.5 | −4.8 | −2.4 | −3.0 | −2.1 | 2.2 | 4.8 | 2.5 | 4.3 |
| B2PLYP | −2.2 | −3.7 | 0.2 | −4.7 | −2.1 | −5.2 | −4.4 | −5.0 | −3.5 | 3.5 | 5.2 | 3.8 | 4.7 |
| MPWB1 K | 0.6 | −1.3 | 0.7 | −2.2 | −1.5 | −4.1 | −1.4 | −2.3 | −1.8 | 1.8 | 4.1 | 2.0 | 3.7 |
| BB1 K | 0.2 | −1.8 | 0.2 | −2.8 | −2.1 | −4.6 | −1.9 | −2.9 | −2.5 | 2.1 | 4.6 | 2.3 | 4.2 |
| BB95 | −2.8 | −4.8 | −4.5 | −7.2 | −6.8 | −8.4 | −5.3 | −7.6 | −7.6 | 6.1 | 8.4 | 6.8 | 7.6 |
| M08-HX | 3.7 | 1.8 | 3.1 | 1.1 | 0.5 | −1.4 | 2.1 | 1.3 | 1.1 | 1.8 | 3.7 | 2.0 | 4.1 |
aAll tested substance geometries were pre-optimized with B3LYP/6-31G(d) functional, then were calculated single-point energy using 6-311 + G(3df,2p) basis set.
bMUE (mean unsigned error) = mean absolute deviation; MaxAE = max absolute error.
cThe relative values of MUEs were calculated from it and the average of BDE values of all N−H substances; the relative values of MaxAE were calculated from it and its corresponding value for BDE of N−H substances.
3.4. The bond dissociation energy of O−H
BDEs of ArO−H substances were calculated here for 4-chlorophenol (OH1) [72], 2-methoxyphenol (OH2) [73], 4-methoxy-2,3,6-trimethylphenol (OH3) [74], 2,2,5,7,8-pentamethyl-chroman-6-ol (OH4) [74], 2,3,5-trimethyl-chroman-4-ol (OH5) [69], ubiquinol-2 (OH6) [69], 2,2,6,7-tetramethyl-2,3-dihydrobenzofuran-5-ol (OH7) [69], 1-hydroxynaphthalene (OH8) [75], 4,4′-dihydroxybiphenyl (OH9) [75] and 1-hydroxy-phenanthrene (OH10) [69] that all have accurate experimental BDE values in the literature [47] (electronic supplementary material, table S9). The comparison of density functionals is presented in table 3.
Table 3.
Absolute (in kcal mol−1)a and relative (in %) deviation of O−H BDEs from experimental values.
| O-H BDE | OH1 | OH2 | OH3 | OH4 | OH5 | OH6 | OH7 | OH8 | OH9 | OH10 | absolute values |
relative valuesc |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MUEb | MaxAEb | MUEb | MaxAEb | |||||||||||
| M06-2X | −2.2 | 1.8 | 0.0 | −0.1 | −0.7 | 3.1 | 0.3 | −1.7 | 1.2 | −0.6 | 1.2 | 3.3 | 1.4 | 4.0 |
| M05-2X | −2.6 | 1.8 | −0.4 | −0.5 | −1.1 | 2.9 | 0.0 | −2.8 | 0.8 | −1.4 | 1.4 | 3.2 | 1.7 | 3.9 |
| M06 | −6.8 | −2.4 | −4.2 | −4.6 | −5.1 | −0.7 | −4.0 | −6.4 | −3.8 | −5.4 | 4.2 | 6.7 | 5.1 | 7.4 |
| M05 | −8.8 | −4.9 | −6.3 | −6.8 | −7.3 | −3.2 | −6.3 | −9.1 | −5.7 | −8.1 | 6.6 | 9.1 | 7.9 | 10.8 |
| BMK | −5.7 | −2.0 | −3.9 | −4.2 | −4.7 | −0.9 | −3.8 | −5.5 | −2.7 | −4.5 | 3.7 | 5.7 | 4.4 | 6.3 |
| MPW1B95 | −5.4 | −1.1 | −3.0 | −3.3 | −3.8 | −0.3 | −2.8 | −4.7 | −2.3 | −3.9 | 3.0 | 5.4 | 3.6 | 5.9 |
| B1B95 | −6.2 | −2.0 | −3.8 | −4.1 | −4.7 | −1.3 | −3.6 | −5.5 | −3.1 | −4.7 | 3.8 | 6.1 | 4.6 | 6.8 |
| B98 | −7.9 | −4.0 | −5.7 | −6.2 | −6.6 | −3.2 | −5.7 | −7.5 | −5.0 | −6.7 | 5.8 | 7.9 | 7.0 | 8.8 |
| B97-2 | −8.4 | −4.5 | −6.2 | −6.7 | −7.2 | −3.9 | −6.1 | −7.9 | −5.2 | −7.1 | 6.3 | 8.3 | 7.5 | 9.2 |
| LC-ωPBE | −7.4 | −3.1 | −4.8 | −5.0 | −5.6 | −1.5 | −4.3 | −7.9 | −3.5 | −6.5 | 4.8 | 7.8 | 5.9 | 9.3 |
| B3LYP | −7.8 | −3.7 | −5.5 | −6.0 | −6.5 | −2.5 | −5.5 | −7.3 | −4.9 | −6.6 | 5.6 | 7.9 | 6.8 | 8.7 |
| cam-B3LYP | −6.0 | −1.5 | −3.6 | −3.9 | −4.4 | 0.0 | −3.4 | −5.8 | −2.6 | −4.7 | 3.6 | 5.9 | 4.3 | 6.6 |
| B2PLYP | −14.3 | −9.3 | −11.0 | −11.3 | −11.8 | −6.7 | −10.7 | −14.0 | −10.8 | −13.0 | 11.3 | 14.4 | 13.6 | 15.9 |
| MPWB1 K | −4.6 | 0.0 | −2.0 | −2.2 | −2.8 | 1.1 | −1.7 | −4.0 | −1.4 | −3.1 | 2.3 | 4.6 | 2.7 | 5.1 |
| BB1 K | −5.2 | −0.6 | −2.6 | −2.8 | −3.4 | 0.4 | −2.3 | −4.6 | −1.9 | −3.6 | 2.7 | 5.1 | 3.2 | 5.7 |
| BB95 | −9.4 | −5.8 | −7.4 | −8.0 | −8.5 | −5.9 | −7.5 | −8.4 | −7.0 | −8.1 | 7.5 | 9.4 | 9.1 | 10.4 |
| M08-HX | −1.6 | 2.2 | 0.6 | 0.4 | −0.1 | 3.2 | 0.8 | −1.2 | 1.7 | 0.0 | 1.3 | 3.4 | 1.5 | 4.1 |
aAll tested substance geometries were pre-optimized with B3LYP/6-31G(d) functional, then were calculated single-point energy using 6-311 + G(3df,2p) basis set.
bMUE (mean unsigned error) = mean absolute deviation; MaxAE = max absolute error.
cThe relative values of MUEs were calculated from it and the average of BDE values of all O−H substances; the relative values of MaxAE were calculated from it and its corresponding value for BDE of O−H substance.
M06-2X, M05-2X and M08-HX functionals give the best performance for O−H BDE calculations. All three functionals have similar MUE and Max AE in both absolute as well as relative values. MUEs of the three functionals above range from 1.2 kcal mol−1 to 1.4 kcal mol−1 (corresponding to 1.4% to 1.7% in relative value) and MaxAEs range from 3.2 kcal mol−1 to 3.4 kcal mol−1 (corresponding to 3.9% to 4.1% in relative value). Based on the computed data, BMK and MPWB1 K are not as good as in the N−H BDE calculations. BMK has MUE and MaxAE of 3.7 kcal mol−1 and 5.7 kcal mol−1, respectively, while those of MPWB1 K are 2.3 kcal mol−1 for MUE and 4.6 kcal mol−1 for MaxAE. B2PLYP is also not good for N−H BDE calculations, with MUE = 11.3 kcal mol−1. Thus, this suggests that BMK, MPWB1 K and B2PLYP should not be used to compute the BDE value of the N−H bond.
3.5. The bond dissociation energy of S−H
For the S−H bond calculations, nine ArS−H substances were selected from the literature (electronic supplementary material, table S11) [47]: benzenethiol (SH1) [69], 2-methyl-benzenethiol (SH2) [76], 3-trifluoromethyl-benzenethiol (SH3) [69], 4-amino-benzenethiol (SH4) [76], 1-naphthalenethiol (SH5) [69], 4-chloro-benzenethiol (SH6) [76], 4-methyl-benzenethiol (SH7) [76], 4-methoxy-benzenethiol (SH8) [76] and 4-ethoxy-benzenethiol (SH9) [69]. The results are presented in table 4.
Table 4.
Absolute (in kcal mol−1)a and relative (in %) deviation of S−H BDEs from reference values.
| S-H BDE | SH1 | SH2 | SH3 | SH4 | SH5 | SH6 | SH7 | SH8 | SH9 | absolute values |
relative valuesc |
||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MUEb | MaxAEb | MUEb | MaxAEb | ||||||||||
| M06-2X | −0.8 | 0.7 | 0.0 | 5.4 | −0.4 | 0.2 | 0.5 | −0.1 | −2.1 | 1.1 | 5.4 | 1.5 | 7.8 |
| M05-2X | −2.1 | −0.4 | −1.2 | 4.5 | −1.7 | −1.0 | −0.8 | −1.0 | −3.0 | 1.8 | 4.5 | 2.3 | 6.5 |
| M06 | −2.5 | −1.5 | −1.6 | 3.1 | −2.8 | −1.7 | −1.4 | −2.3 | −4.3 | 2.3 | 4.3 | 3.0 | 5.4 |
| M05 | −3.3 | −2.3 | −2.4 | 2.5 | −3.9 | −2.3 | −2.1 | −3.2 | −5.2 | 3.0 | 5.2 | 3.9 | 6.6 |
| BMK | −2.6 | −1.1 | −1.7 | 3.4 | −2.5 | −1.6 | −1.4 | −2.0 | −4.0 | 2.3 | 4.0 | 2.9 | 5.0 |
| MPW1B95 | 7.6 | −0.6 | 7.6 | 3.6 | −2.1 | −1.1 | −0.8 | −1.7 | −3.7 | 3.2 | 7.6 | 4.1 | 9.4 |
| B1B95 | 7.1 | −1.2 | 7.1 | 3.0 | −2.7 | −1.6 | −1.3 | −2.3 | −4.3 | 3.4 | 7.1 | 4.3 | 8.8 |
| B98 | −4.1 | −2.8 | −3.1 | 1.8 | −4.3 | −3.1 | −2.9 | −3.6 | −5.6 | 3.5 | 5.6 | 4.5 | 7.1 |
| B97-2 | −4.1 | −2.9 | −3.1 | 1.6 | −4.4 | −3.2 | −2.9 | −3.8 | −5.9 | 3.5 | 5.9 | 4.5 | 7.4 |
| LC-wPBE | −3.5 | −2.0 | −2.8 | 3.7 | −3.4 | −2.3 | −2.0 | −2.1 | −4.0 | 2.9 | 4.0 | 3.7 | 5.1 |
| B3LYP | 6.0 | −2.5 | −2.7 | 2.0 | −3.9 | −2.8 | −2.6 | −3.3 | −5.4 | 3.5 | 6.0 | 4.5 | 7.4 |
| cam-B3LYP | −2.7 | −1.2 | −1.8 | 4.0 | −2.5 | −1.5 | −1.4 | −1.6 | −3.6 | 2.2 | 4.0 | 2.9 | 5.8 |
| B2PLYP | −1.2 | −9.0 | −9.9 | -7.3 | −10.5 | −9.4 | −9.2 | −8.8 | −10.8 | 8.5 | 10.8 | 10.8 | 13.7 |
| MPWB1 K | 7.6 | −0.3 | 7.6 | 4.4 | −1.8 | −0.8 | −0.6 | −1.1 | −3.1 | 3.0 | 7.6 | 3.9 | 9.5 |
| BB1 K | 7.2 | −0.7 | 7.3 | 4.0 | −2.2 | −1.2 | −0.9 | −1.5 | −3.5 | 3.1 | 7.3 | 4.0 | 9.0 |
| BB95 | −3.5 | −2.6 | −2.3 | 0.5 | −4.4 | −3.1 | −2.6 | −4.6 | −6.6 | 3.4 | 6.6 | 4.3 | 8.4 |
| M08-HX | 0.1 | 1.6 | 1.0 | 6.2 | 0.5 | 1.1 | 1.4 | 0.9 | −1.1 | 1.6 | 6.2 | 2.0 | 8.9 |
aAll tested substance geometries were pre-optimized with B3LYP/6-31G(d) functional, then were calculated single-point energy using 6-311 + G(3df,2p) basis set.
bMUE (mean unsigned error) = mean absolute deviation; MaxAE = max absolute error.
cThe relative values of MUEs were calculated from it and the average of BDE values of all O−H substances; the relative values of MaxAE were calculated from it and its corresponding value for BDE of O−H substances.
The results show that M06-2X is the best density functional for this dataset. Its MUE and MaxAE are 1.1 kcal mol−1 and 5.4 kcal mol−1, respectively. At the same time, M05-2X and M08-HX also have good accuracy with 1.8 kcal mol−1 and 1.6 kcal mol−1. However, the MaxAE (both absolute and relative values) of those functionals are higher than those of C−H BDEs, N−H BDEs and O−H BDEs calculations, which are in the range of 4.5 kcal mol−1 to 6.2 kcal mol−1. The rest of the tested functionals have similar deviations ranging from 2.3 kcal mol−1 to 3.5 kcal mol−1, except the B2PLYP functional with 8.5 kcal mol−1 of absolute MUE. Thus BDEs of the S−H bond could be calculated effectively by the Minnesota functionals.
3.6. Average deviation
To evaluate the accuracy and effectiveness of the computational methods, the average of MUE and MaxAE of each functional were compared for an overall assessment of their performance in calculating the BDE values. The results are presented in table 5.
Table 5.
The average absolute (in kcal mol−1) and relative (in %) deviation of BDE values from the experimental reference values.
| functionals | absolute value |
relative value |
||
|---|---|---|---|---|
| MUE | MaxAE | MUE | MaxAE | |
| M06-2X | 1.2 | 3.7 | 1.4 | 4.5 |
| M05-2X | 1.4 | 3.7 | 1.6 | 4.6 |
| M06 | 3.2 | 5.5 | 3.7 | 6.2 |
| M05 | 4.6 | 7.0 | 5.4 | 8.1 |
| BMK | 2.3 | 4.4 | 2.7 | 5.0 |
| MPW1B95 | 2.7 | 5.8 | 3.2 | 6.6 |
| B1B95 | 3.2 | 6.2 | 3.9 | 7.0 |
| B98 | 4.3 | 6.8 | 5.1 | 7.6 |
| B97-2 | 4.7 | 7.2 | 5.6 | 8.1 |
| LC-ωPBE | 3.3 | 5.9 | 3.9 | 6.7 |
| B3LYP | 4.3 | 6.8 | 5.0 | 7.6 |
| cam-B3LYP | 2.5 | 4.9 | 2.9 | 5.6 |
| B2PLYP | 8.2 | 10.6 | 9.8 | 12.2 |
| MPWB1 K | 2.2 | 5.3 | 2.6 | 6.0 |
| BB1 K | 2.5 | 5.6 | 3.0 | 6.3 |
| BB95 | 5.5 | 8.2 | 6.5 | 9.1 |
| M08-HX | 1.5 | 4.1 | 1.7 | 5.3 |
M06-2X, M05-2X and M08-HX yield higher accuracy than any other tested functionals. M06-2X is the best performer, with the lowest absolute and relative value deviation. M05-2X and M08-HX have good accuracy; however, the MUE and MaxAE values are slightly higher than those of M06-2X. All of the other functionals have higher errors in BDEs compared to the Minnesota functionals. Therefore, based on computed data, the M06-2X, M05-2X and M08-HX functionals offer the most effective and accurate methods to compute BDE values of ArX−H (X=C, N, O, S) bonds.
4. Conclusion
In this study, the BDE(X−H, X=C, N, O, S) values of typical aromatic compounds have been computed and compared using 17 different DFT functionals, based on the protocol by Wright [11]. The results showed that M06-2X, M05-2X and M08-HX yield higher accuracy BDE values than any other tested functionals. The 6-31G(d) basis set with the B3LYP functional is sufficient for geometry optimization, with acceptable absolute and relative value deviation, whereas the larger basis sets 6-31 + G(d), 6-31 + G(d,p), 6-311G(d,p) and 6-311 ++ G(d,p) did not deliver a significant difference. Hence, it is possible to obtain reasonable predictions of BDE of very large molecules based on geometry optimization with a small basis set without substantial loss of accuracy. It should be emphasized that this does not apply to the single-point BDE calculation, where a large basis set was used for accurate results. When compared to experimental data, the M06-2X, M05-2X and M08-HX density functionals were found to offer the most affordable performance for calculating the BDEs of ArX−H (X=C, N, O, S) bonds.
Data accessibility
All relevant necessary data to reproduce all results in the paper are within the main text, electronic supplementary material (https://doi.org/10.5281/zenodo.6052741) and the Dryad Digital Repository: https://doi.org/10.5061/dryad.1c59zw3x3 [77].
Authors' contributions
N.Q.T.: data curation, formal analysis, investigation, validation and visualization; A.M.: resources, software, supervision, validation, visualization and writing—review and editing; N.T.H.: data curation, formal analysis, investigation, validation and visualization; Q.V.V.: conceptualization, formal analysis, investigation, methodology, project administration, resources, software, supervision, validation, visualization, writing—original draft and writing—review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed therein.
Conflict of interest declaration
We declare we have no competing interests.
Funding
N.Q.T. was funded by Vingroup JSC and supported by the Master, PhD Scholarship Programme of Vingroup Innovation Foundation (VINIF), Institute of Big Data, code VINIF.2021.TS.114. This research was funded by the Vietnamese Ministry of Education and Training under project no. B2021-DNA-16 (Q.V.V.).
References
- 1.Zhao Y, Schultz NE, Truhlar DG. 2006. Design of density functionals by combining the method of constraint satisfaction with parametrization for thermochemistry, thermochemical kinetics, and noncovalent interactions. J. Chem. Theory Comput. 2, 364-382. ( 10.1021/ct0502763) [DOI] [PubMed] [Google Scholar]
- 2.Pople JA, Gill PM, Johnson BG. 1992. Kohn—Sham density-functional theory within a finite basis set. Chem. Phys. Lett. 199, 557-560. ( 10.1016/0009-2614(92)85009-Y) [DOI] [Google Scholar]
- 3.Becke AD. 1993. A new mixing of Hartree–Fock and local density-functional theories. J. Chem. Phys. 98, 1372-1377. ( 10.1063/1.464304) [DOI] [Google Scholar]
- 4.Becke AD. 1992. Density-functional thermochemistry. I. The effect of the exchange-only gradient correction. J. Chem. Phys. 96, 2155-2160. ( 10.1063/1.462066) [DOI] [Google Scholar]
- 5.Becke AD. 1996. Density-functional thermochemistry. IV. A new dynamical correlation functional and implications for exact-exchange mixing. J. Chem. Phys. 104, 1040-1046. ( 10.1063/1.470829) [DOI] [Google Scholar]
- 6.Lodewyk MW, Siebert MR, Tantillo DJ. 2012. Computational prediction of 1H and 13C chemical shifts: a useful tool for natural product, mechanistic, and synthetic organic chemistry. Chem. Rev. 112, 1839-1862. ( 10.1021/cr200106v) [DOI] [PubMed] [Google Scholar]
- 7.Sharma A, Suryanarayana P. 2021. Real-space density functional theory adapted to cyclic and helical symmetry: application to torsional deformation of carbon nanotubes. Phys. Rev. B 103, 035101. ( 10.1103/PhysRevB.103.035101) [DOI] [Google Scholar]
- 8.Verma P, Truhlar DG. 2020. Status and challenges of density functional theory. Trends Chem. 2, 302-318. ( 10.1016/j.trechm.2020.02.005) [DOI] [Google Scholar]
- 9.de Oliveira MT, Alves JIM, Braga AA, Wilson DJ, Barboza CA. 2021. Do Double-Hybrid Exchange–Correlation Functionals Provide Accurate Chemical Shifts? A Benchmark Assessment for Proton NMR. J. Chem. Theory Comput. 17, 6876-6885. ( 10.1021/acs.jctc.1c00604) [DOI] [PubMed] [Google Scholar]
- 10.DiLabio G, Pratt D. 2000. Density functional theory based model calculations for accurate bond dissociation enthalpies. 2. Studies of X−X and X−Y (X, Y=C, N, O, S, Halogen) Bonds. J. Phys. Chem. A 104, 1938-1943. ( 10.1021/jp9938617) [DOI] [Google Scholar]
- 11.DiLabio G, Pratt D, LoFaro A, Wright JS. 1999. Theoretical study of X−H bond energetics (X=C, N, O, S): application to substituent effects, gas phase acidities, and redox potentials. J. Phys. Chem. A 103, 1653-1661. ( 10.1021/jp984369a) [DOI] [Google Scholar]
- 12.Koch W, Holthausen MC. 2015. A chemist's guide to density functional theory. New York, NY: John Wiley & Sons. [Google Scholar]
- 13.Curtiss LA, Raghavachari K, Trucks GW, Pople JA. 1991. Gaussian-2 theory for molecular energies of first- and second-row compounds. J. Chem. Phys. 94, 7221-7230. ( 10.1063/1.460205) [DOI] [Google Scholar]
- 14.Froese RD, Humbel S, Svensson M, Morokuma K. 1997. IMOMO(G2MS): a new high-level G2-like method for large molecules and its applications to Diels−Alder reactions. J. Phys. Chem. A 101, 227-233. ( 10.1021/jp963019q) [DOI] [Google Scholar]
- 15.Humbel S, Sieber S, Morokuma K. 1996. The IMOMO method: integration of different levels of molecular orbital approximations for geometry optimization of large systems: test for n-butane conformation and SN2 reaction: RCl+Cl−. J. Chem. Phys. 105, 1959-1967. ( 10.1063/1.472065) [DOI] [Google Scholar]
- 16.de Souza GL, Peterson KA. 2021. Benchmarking antioxidant-related properties for gallic acid through the use of DFT, MP2, CCSD, and CCSD(T) approaches. J. Phys. Chem. A 125, 198-208. ( 10.1021/acs.jpca.0c09116) [DOI] [PubMed] [Google Scholar]
- 17.Fang Z, Vasiliu M, Peterson KA, Dixon DA. 2017. Prediction of bond dissociation energies/heats of formation for diatomic transition metal compounds: CCSD(T) works. J. Chem. Theory Comput. 13, 1057-1066. ( 10.1021/acs.jctc.6b00971) [DOI] [PubMed] [Google Scholar]
- 18.do Couto PC, Cabral BJC, Simoes JAM. 2006. S–H bond dissociation enthalpies: the importance of a complete basis set approach. Chem. Phys. Lett. 421, 504-507. ( 10.1016/j.cplett.2006.02.009) [DOI] [Google Scholar]
- 19.Yao XQ, Hou XJ, Jiao H, Xiang HW, Li YW. 2003. Accurate calculations of bond dissociation enthalpies with density functional methods. J. Phys. Chem. A 107, 9991-9996. ( 10.1021/jp0361125) [DOI] [Google Scholar]
- 20.Chandra AK, Nam PC, Nguyen MT. 2003. The S−H bond dissociation enthalpies and acidities of para and meta substituted thiophenols: a quantum chemical study. J. Phys. Chem. A 107, 9182-9188. ( 10.1021/jp035622w) [DOI] [Google Scholar]
- 21.Zhao Y, Truhlar DG. 2008. How well can new-generation density functionals describe the energetics of bond-dissociation reactions producing radicals?. J. Phys. Chem. A 112, 1095-1099. ( 10.1021/jp7109127) [DOI] [PubMed] [Google Scholar]
- 22.Zhao Y, Schultz NE, Truhlar DG. 2005. Exchange-correlation functional with broad accuracy for metallic and nonmetallic compounds, kinetics, and noncovalent interactions. J. Chem. Phys. 123, 161103. ( 10.1063/1.2126975) [DOI] [PubMed] [Google Scholar]
- 23.Vo QV, Tam NM, Van Bay M, Thong NM, Le Huyen T, Hoa NT, Mechler A. 2020. The antioxidant activity of natural diterpenes: theoretical insights. Rsc Adv. 10, 14 937-14 943. ( 10.1039/D0RA02681F) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Vo QV, Thong NM, Le Huyen T, Nam PC, Tam NM, Hoa NT, Mechler A. 2020. A thermodynamic and kinetic study of the antioxidant activity of natural hydroanthraquinones. Rsc Adv. 10, 20 089-20 097. ( 10.1039/D0RA04013D) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Galano A, Raúl Alvarez-Idaboy J. 2019. Computational strategies for predicting free radical scavengers' protection against oxidative stress: where are we and what might follow?. Int. J. Quantum Chem. 119, e25665. ( 10.1002/qua.25665) [DOI] [Google Scholar]
- 26.Galano A, Alvarez-Idaboy JR. 2014. Kinetics of radical-molecule reactions in aqueous solution: a benchmark study of the performance of density functional methods. J. Comput. Chem. 35, 2019-2026. ( 10.1002/jcc.23715) [DOI] [PubMed] [Google Scholar]
- 27.Galano A, Alvarez-Idaboy JR. 2013. A computational methodology for accurate predictions of rate constants in solution: application to the assessment of primary antioxidant activity. J. Comput. Chem. 34, 2430-2445. ( 10.1002/jcc.23409) [DOI] [PubMed] [Google Scholar]
- 28.Hoa NT, Van Bay M, Mechler A, Vo QV. 2022. Theoretical insights into the antiradical activity and copper-catalysed oxidative damage of mexidol in the physiological environment. R. Soc. Open Sci. 9, 211239. ( 10.1098/rsos.211239) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Galano A, Mazzone G, Alvarez-Diduk R, Marino T, Alvarez-Idaboy JR, Russo N. 2016. Food antioxidants: chemical insights at the molecular level. Annu. Rev. Food Sci. Technol. 7, 335-352. ( 10.1146/annurev-food-041715-033206) [DOI] [PubMed] [Google Scholar]
- 30.Vo QV, Hoa NT, Nam PC, Quang DT, Mechler A. 2020. In silico evaluation of the radical scavenging mechanism of mactanamide. ACS Omega 5, 24 106-24 110. ( 10.1021/acsomega.0c03646) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Allen NS, Zeynalov EB, Taylor K, Birkett P. 2009. Antioxidant capacity of novel amine derivatives of buckminsterfullerene: determination of inhibition rate constants in a model oxidation system. Polym. Degrad. Stab. 94, 1932-1940. ( 10.1016/j.polymdegradstab.2009.08.002) [DOI] [Google Scholar]
- 32.Vo QV, Hoa NT, Mechler A. 2021. Modelling the mechanism and kinetics of the radical scavenging activity of iminostilbene. Polym. Degrad. Stab. 185, 109483. ( 10.1016/j.polymdegradstab.2021.109483) [DOI] [Google Scholar]
- 33.Carreon-Gonzalez M, Vivier-Bunge A, Alvarez-Idaboy JR. 2019. Thiophenols, promising scavengers of peroxyl radicals: mechanisms and kinetics. J. Comput. Chem. 40, 2103-2110. ( 10.1002/jcc.25862) [DOI] [PubMed] [Google Scholar]
- 34.Carreon-Gonzalez M, Muñoz-Rugeles L, Vivier-Bunge A, Alvarez-Idaboy JR. 2022. Chemical repair of damaged leucine and tryptophane by thiophenols at close to diffusion-controlled rates: mechanisms and kinetics. J. Com. Chem. 43, 556-567. ( 10.1002/jcc.26813) [DOI] [PubMed] [Google Scholar]
- 35.Zhao Y, Truhlar DG. 2011. Density functional theory for reaction energies: test of meta and hybrid meta functionals, range-separated functionals, and other high-performance functionals. J. Chem. Theory Comput. 7, 669-676. ( 10.1021/ct1006604) [DOI] [Google Scholar]
- 36.Yu H, Truhlar DG. 2015. Components of the bond energy in polar diatomic molecules, radicals, and ions formed by group-1 and group-2 metal atoms. J. Chem. Theory Comput. 11, 2968-2983. ( 10.1021/acs.jctc.5b00083) [DOI] [PubMed] [Google Scholar]
- 37.Bras NF, Perez MA, Fernandes PA, Silva PJ, Ramos MJ. 2011. Accuracy of density functionals in the prediction of electronic proton affinities of amino acid side chains. J. Chem. Theory Comput. 7, 3898-3908. ( 10.1021/ct200309v) [DOI] [PubMed] [Google Scholar]
- 38.Wodrich MD, Corminboeuf C, Schreiner PR, Fokin AA, Schleyer PvR. 2007. How accurate are DFT treatments of organic energies?. Org. Lett. 9, 1851-1854. ( 10.1021/ol070354w) [DOI] [PubMed] [Google Scholar]
- 39.Rokob TA, Hamza A, Pápai I. 2007. Computing reliable energetics for conjugate addition reactions. Org. Lett. 9, 4279-4282. ( 10.1021/ol701872z) [DOI] [PubMed] [Google Scholar]
- 40.Zhao Y, Truhlar DG. 2008. Density functionals with broad applicability in chemistry. Acc. Chem. Res. 41, 157-167. ( 10.1021/ar700111a) [DOI] [PubMed] [Google Scholar]
- 41.Fattahi A, Lis L, Kass SR. 2016. Phenylcyclopropane energetics and characterization of its conjugate base: phenyl substituent effects and the C–H bond dissociation energy of cyclopropane. J. Org. Chem. 81, 9175-9179. ( 10.1021/acs.joc.6b01718) [DOI] [PubMed] [Google Scholar]
- 42.Bach RD, Schlegel HB. 2021. The Bond Dissociation Energy of the N–O Bond. J. Phys. Chem. A 125, 5014-5021. ( 10.1021/acs.jpca.1c02741) [DOI] [PubMed] [Google Scholar]
- 43.Jana MK, Singh A, Late DJ, Rajamathi CR, Biswas K, Felser C, Waghmare UV, Rao C. 2015. A combined experimental and theoretical study of the structural, electronic and vibrational properties of bulk and few-layer Td-WTe2. J. Condens. Matter Phys. 27, 285401. ( 10.1088/0953-8984/27/28/285401) [DOI] [PubMed] [Google Scholar]
- 44.Patra A, Kapse S, Thapa R, Late DJ, Rout CS. 2022. Quasi-one-dimensional van der Waals TiS3 nanosheets for energy storage applications: theoretical predications and experimental validation. Appl. Phys. Lett. 120, 103102. ( 10.1063/5.0080346) [DOI] [Google Scholar]
- 45.Late DJ, Shirodkar SN, Waghmare UV, Dravid VP, Rao C. 2014. Thermal expansion, anharmonicity and temperature-dependent Raman spectra of single- and few-layer MoSe2 and WSe2. ChemPhysChem 15, 1592-1598. ( 10.1002/cphc.201400020) [DOI] [PubMed] [Google Scholar]
- 46.Johnson ER, Clarkin OJ, DiLabio GA. 2003. Density functional theory based model calculations for accurate bond dissociation enthalpies. 3. A single approach for X−H, X−X, and X−Y (X, Y=C, N, O, S, halogen) bonds. J. Phys. Chem. A 107, 9953-9963. ( 10.1021/jp035315q) [DOI] [Google Scholar]
- 47.Luo YR. 2002. Handbook of bond dissociation energies in organic compounds. Boca Raton, FL: CRC Press. [Google Scholar]
- 48.Hehre W, Yu J, Klunzinger P, Lou L. Spartan Software. Wavefunction 2000 Inc., Irvine.
- 49.Tirado-Rives J, Jorgensen WL. 2008. Performance of B3LYP density functional methods for a large set of organic molecules. J. Chem. Theory Comput. 4, 297-306. ( 10.1021/ct700248k) [DOI] [PubMed] [Google Scholar]
- 50.Klein E, Lukeš V. 2006. DFT/B3LYP study of O–H bond dissociation enthalpies of para and meta substituted phenols: correlation with the phenolic C–O bond length. J. Mol. Struct. 767, 43-50. ( 10.1016/j.theochem.2006.04.017) [DOI] [Google Scholar]
- 51.Puzzarini C, Biczysko M, Barone V. 2010. Accurate harmonic/anharmonic vibrational frequencies for open-shell systems: performances of the B3LYP/N07D model for semirigid free radicals benchmarked by CCSD(T) computations. J. Chem. Theory Comput. 6, 828-838. ( 10.1021/ct900594h) [DOI] [PubMed] [Google Scholar]
- 52.Carbonniere P, Lucca T, Pouchan C, Rega N, Barone V. 2005. Vibrational computations beyond the harmonic approximation: performances of the B3LYP density functional for semirigid molecules. J. Comput. Chem. 26, 384-388. ( 10.1002/jcc.20170) [DOI] [PubMed] [Google Scholar]
- 53.Ervin KM, DeTuri VF. 2002. Anchoring the gas-phase acidity scale. J. Phys. Chem. A 106, 9947-9956. ( 10.1021/jp020594n) [DOI] [Google Scholar]
- 54.Berkowitz J, Ellison GB, Gutman D. 1994. Three methods to measure RH bond energies. J. Phys. Chem. 98, 2744-2765. ( 10.1021/j100062a009) [DOI] [Google Scholar]
- 55.McMillen DF, Golden DM. 1982. Hydrocarbon bond dissociation energies. Annu. Rev. Phys. Chem. 33, 493-532. ( 10.1146/annurev.pc.33.100182.002425) [DOI] [Google Scholar]
- 56.Parker VD, Handoo KL, Roness F, Tilset M. 1991. Electrode potentials and the thermodynamics of isodesmic reactions. J. Am. Chem. Soc. 113, 7493-7498. ( 10.1021/ja00020a007) [DOI] [Google Scholar]
- 57.Brocks JJ, Beckhaus HD, Beckwith AL, Rüchardt C. 1998. Estimation of bond dissociation energies and radical stabilization energies by ESR spectroscopy. J. Org. Chem. 63, 1935-1943. ( 10.1021/jo971940d) [DOI] [Google Scholar]
- 58.Kromkin E, Tumanov V, Denisov E. 2002. Evaluation of CH bond dissociation energies in alkylaromatic hydrocarbons and the enthalpies of corresponding radicals from kinetic data. Pet. Chem. 42, 1-11. [Google Scholar]
- 59.Reed DR, Kass SR. 2000. Experimental determination of the α and β C—H bond dissociation energies in naphthalene. J. Mass Spectrom. 35, 534-539. () [DOI] [PubMed] [Google Scholar]
- 60.Stein SE, Brown R. 1991. Prediction of carbon-hydrogen bond dissociation energies for polycyclic aromatic hydrocarbons of arbitrary size. J. Am. Chem. Soc. 113, 787-793. ( 10.1021/ja00003a009) [DOI] [Google Scholar]
- 61.Arnett EM, Flowers RA, Ludwig RT, Meekhof AE, Walek SA. 1997. Triarylmethanes and 9-arylxanthenes as prototypes amphihydric compounds for relating the stabilities of cations, anions and radicals by C-H bond cleavage and electron transfer. J. Phys. Org. Chem. 10, 499-513. () [DOI] [Google Scholar]
- 62.Izgorodina EI, Brittain DR, Hodgson JL, Krenske EH, Lin CY, Namazian M, Coote ML. 2007. Should contemporary density functional theory methods be used to study the thermodynamics of radical reactions?. J. Phys. Chem. A 111, 10 754-10 768. ( 10.1021/jp075837w) [DOI] [PubMed] [Google Scholar]
- 63.Jonsson M, Lind J, Eriksen TE, Merenyi G. 1994. Redox and acidity properties of 4-substituted aniline radical cations in water. J. Am. Chem. Soc. 116, 1423-1427. ( 10.1021/ja00083a030) [DOI] [Google Scholar]
- 64.Bordwell F, Zhang XM, Cheng JP. 1993. Bond dissociation energies of the nitrogen-hydrogen bonds in anilines and in the corresponding radical anions. Equilibrium acidities of aniline radical cations. J. Org. Chem. 58, 6410-6416. ( 10.1021/jo00075a041) [DOI] [Google Scholar]
- 65.Cheng J, Lu Y, Liu B, Zhao Y, Wang D, Sun Y, Mi J. 1998. Effects of double substitution on the thermodynamic stabilities of nitrogen radicals. Sci. China Ser. B 41, 215-224. ( 10.1007/BF02877810) [DOI] [Google Scholar]
- 66.MacFaul PA, Wayner D, Ingold K. 1997. Measurement of N−H bond strengths in aromatic amines by photoacoustic calorimetry1. J. Org. Chem. 62, 3413-3414. ( 10.1021/jo962195s) [DOI] [PubMed] [Google Scholar]
- 67.Zhang XM, Bordwell FG. 1994. Acidities and homolytic bond dissociation enthalpies (BDEs) of the acidic H-A bonds in acyclic and cyclic alkoxycarbonyl compounds (esters and carbamates). J. Org. Chem. 59, 6456-6458. ( 10.1021/jo00100a058) [DOI] [Google Scholar]
- 68.Bordwell F, Zhang S, Zhang XM, Liu WZ. 1995. Homolytic bond dissociation enthalpies of the acidic H-A bonds caused by proximate substituents in sets of methyl ketones, carboxylic esters, and carboxamides related to changes in ground state energies. J. Am. Chem. Soc. 117, 7092-7096. ( 10.1021/ja00132a008) [DOI] [Google Scholar]
- 69.Denisov E, Denisova T. 2000. Handbook of Antioxidants. London, New York: CRC Press. [Google Scholar]
- 70.Arnett EM, Venimadhavan S, Amarnath K. 1992. Homolytic and heterolytic cleavage energies for carbon-nitrogen bonds. J. Am. Chem. Soc. 114, 5598-5602. ( 10.1021/ja00040a018) [DOI] [Google Scholar]
- 71.Lucarini M, Pedrielli P, Pedulli GF, Valgimigli L, Gigmes D, Tordo P. 1999. Bond dissociation energies of the N−H bond and rate constants for the reaction with alkyl, alkoxyl, and peroxyl radicals of phenothiazines and related compounds. J. Am. Chem. Soc. 121, 11 546-11 553. ( 10.1021/ja992904u) [DOI] [Google Scholar]
- 72.Bordwell FG, Cheng J. 1991. Substituent effects on the stabilities of phenoxyl radicals and the acidities of phenoxyl radical cations. J. Am. Chem. Soc. 113, 1736-1743. ( 10.1021/ja00005a042) [DOI] [Google Scholar]
- 73.de Heer MI, Korth HG, Mulder P. 1999. Poly methoxy phenols in solution: O−H bond dissociation enthalpies, structures, and hydrogen bonding. J. Org. Chem. 64, 6969-6975. ( 10.1021/jo9901485) [DOI] [Google Scholar]
- 74.Lucarini M, Pedrielli P, Pedulli GF, Cabiddu S, Fattuoni C. 1996. Bond dissociation energies of O−H bonds in substituted phenols from equilibration studies. J. Org. Chem. 61, 9259-9263. ( 10.1021/jo961039i) [DOI] [Google Scholar]
- 75.Mahoney L, DaRooge M. 1975. Kinetic behavior and thermochemical properties of phenoxy radicals. J. Am. Chem. Soc. 97, 4722-4731. ( 10.1021/ja00849a039) [DOI] [Google Scholar]
- 76.Bordwell F, Zhang XM, Satish A, Cheng JP. 1994. Assessment of the importance of changes in ground-state energies on the bond dissociation enthalpies of the O-H bonds in phenols and the S-H bonds in thiophenols. J. Am. Chem. Soc. 116, 6605-6610. ( 10.1021/ja00094a015) [DOI] [Google Scholar]
- 77.Trung NQ, Mechler A, Hoa NT, Vo QV. 2022. Data from: Calculating bond dissociation energies of X–H (X=C, N, O, S) bonds of aromatic systems via density functional theory: a detailed comparison of methods. Dryad Digital Repository. ( 10.5061/dryad.1c59zw3x3) [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Citations
- Trung NQ, Mechler A, Hoa NT, Vo QV. 2022. Data from: Calculating bond dissociation energies of X–H (X=C, N, O, S) bonds of aromatic systems via density functional theory: a detailed comparison of methods. Dryad Digital Repository. ( 10.5061/dryad.1c59zw3x3) [DOI] [PMC free article] [PubMed]
Data Availability Statement
All relevant necessary data to reproduce all results in the paper are within the main text, electronic supplementary material (https://doi.org/10.5281/zenodo.6052741) and the Dryad Digital Repository: https://doi.org/10.5061/dryad.1c59zw3x3 [77].
