Correlating reactivity and selectivity to cyclopentadienyl ligand properties in Rh(III)-catalyzed C-H activation reactions: An experimental and computational study

Article abstract of DOI:10.1021/jacs.6b11670

Cp^xRh(III)-catalyzed C-H functionalization reactions are a proven method for the efficient assembly of small molecules. However, rationalization of the effects of cyclopentadienyl (Cp^x) ligand structure on reaction rate and selectivity has been viewed as a black box, and a truly systematic study is lacking. Consequently, predicting the outcomes of these reactions is challenging because subtle variations in ligand structure can cause notable changes in reaction behavior. A predictive tool is, nonetheless, of considerable value to the community as it would greatly accelerate reaction development. Designing a data set in which the steric and electronic properties of the Cp^xRh(III) catalysts were systematically varied allowed us to apply multivariate linear regression algorithms to establish correlations between these catalyst-based descriptors and the regio-, diastereoselectivity, and rate of model reactions. This, in turn, led to the development of quantitative predictive models that describe catalyst performance. Our newly described cone angles and Sterimol parameters for Cp^x ligands served as highly correlative steric descriptors in the regression models. Through rational design of training and validation sets, key diastereoselectivity outliers were identified. Computations reveal the origins of the outstanding stereoinduction displayed by these outliers. The results are consistent with partial η⁵-η³ ligand slippage that occurs in the transition state of the selectivity-determining step. In addition to the instructive value of our study, we believe that the insights gained are transposable to other group 9 transition metals and pave the way toward rational design of C-H functionalization catalysts.

Full text of DOI:10.1021/jacs.6b11670

Article
pubs.acs.org/JACS
Correlating Reactivity and Selectivity to Cyclopentadienyl Ligand
Properties in Rh(III)-Catalyzed C−H Activation Reactions: An
Experimental and Computational Study
Tiffany Piou,†,‡,⊥ Fedor Romanov-Michailidis,†,‡,⊥ Maria Romanova-Michaelides,§,# Kelvin E. Jackson,∥,#
Natthawat Semakul,†,‡ Trevor D. Taggart,‡ Brian S. Newell,‡ Christopher D. Rithner,‡
Robert S. Paton,*,∥ and Tomislav Rovis*,†,‡
†Department of Chemistry, Columbia University, New York, New York 10027, United States
‡Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
§Department of Biochemistry, University of Geneva, 1211 Geneva 4, Switzerland
∥Chemistry Research Laboratory, University of Oxford, Mansfield Road, Oxford OX1 3TA, United Kingdom
S
* Supporting Information
ABSTRACT: CpXRh(III)-catalyzed C−H functionalization reactions are a proven method for the efficient assembly of small
molecules. However, rationalization of the effects of cyclopentadienyl (CpX) ligand structure on reaction rate and selectivity has
been viewed as a black box, and a truly systematic study is lacking. Consequently, predicting the outcomes of these reactions is
challenging because subtle variations in ligand structure can cause notable changes in reaction behavior. A predictive tool is,
nonetheless, of considerable value to the community as it would greatly accelerate reaction development. Designing a data set in
which the steric and electronic properties of the CpXRh(III) catalysts were systematically varied allowed us to apply multivariate
linear regression algorithms to establish correlations between these catalyst-based descriptors and the regio-, diastereoselectivity,
and rate of model reactions. This, in turn, led to the development of quantitative predictive models that describe catalyst
performance. Our newly described cone angles and Sterimol parameters for CpX ligands served as highly correlative steric
descriptors in the regression models. Through rational design of training and validation sets, key diastereoselectivity outliers were
identified. Computations reveal the origins of the outstanding stereoinduction displayed by these outliers. The results are
consistent with partial η5−η3 ligand slippage that occurs in the transition state of the selectivity-determining step. In addition to
the instructive value of our study, we believe that the insights gained are transposable to other group 9 transition metals and pave
the way toward rational design of C−H functionalization catalysts.
of reactions to access a variety of heterocycles.8 Among these
transformations, [Cp*RhCl2]2 and its cationic forms are nearly
INTRODUCTION
■
Physical and chemical properties of metal complexes can be
modulated by changing the ligands on the metal center or by
universally employed as the rhodium source and are considered
modifying the substituents on the ligand framework.1
privileged catalysts for such reactions.
The prominence of cyclopentadienyl (Cp) ligands in
Consequently, much effort has been directed toward the
contemporary transition metal chemistry can be traced to
design, synthesis, and use of new ligands that contain
substituents with varying steric and electronic properties.2
Rh(III)-catalyzed C−H activation is an attractive mode of
catalysis for the synthesis of small molecules.3 This approach
has the advantage of low catalyst loading, with reactions often
performed under mild conditions allowing broad functional
group tolerance. Following the seminal work of Miura/Satoh4
and Fagnou,5 our group6 and others7 have developed a plethora
several key features including the large M−Cp dissociation
bond energy. The pentamethylcyclopentadienyl ligand (Cp*) is
certainly the best known Cp derivative. As compared to the
parent Cp ligand, Cp* is sterically more demanding, and the
Received: November 21, 2016
© XXXX American Chemical Society
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methyl groups on Cp* increase the electron density on the
metal center. The increased donation from the Cp* results in a
greater π-backbonding to other ligands. Likewise, Cp*
complexes are more easily oxidized than their Cp analogues.
In comparison with CpRh(III) complexes, Cp*Rh(III)
analogues are soluble in most organic solvents. For these
reasons, Cp*Rh(III) is considered to be the catalyst of choice
for C−H activation reactions.
Initial reports by Fagnou13 and Glorius14 showed the
isotropic [Cp*Rh(III)] catalyst results in a mixture of
regioisomers (generally a 2:1 ratio) for the insertion of
aliphatic alkenes into benzohydroxamate. However, we have
recently shown that a more sterically demanding di-tert-butyl-
cyclopentadienyl ligand (Cpt)15 on rhodium delivers the
dihydroisoquinolinones with synthetically useful regioselectiv-
ities (typically >10:1 regioisomer ratio, eq 3).16 Simultaneously,
Cramer and co-workers reported that a tetrahydroindenyl
ligand (CpCy) also affords exquisite regioselectivities for a
similar transformation.17
We have recently described a cyclopropanation reaction
involving the coupling between N-enoxyphthalimides and
electron-deficient alkenes (eq 4).18 Crucial to the success of
this reaction was the use of a monoisopropylcyclopentadienyl
ligand (CpiPr) for Rh(III), which allows the formation of trans-
disubstituted cyclopropanes in excellent diastereoselectivities.
We later identified a sterically bulky tert-butyl-tetramethylcy-
clopentadienyl ligand (Cp*tBu) capable of driving the reaction
toward an alternative carboamination path delivering the
corresponding adducts instead of the competitive cyclo-
propanation products with excellent chemoselectivities (eq
5).19
Recently, we and others changed this paradigm by identifying
several cyclopentadienyl ligands as alternatives for Cp* (Figure
1). Steric and electronic manipulation of the Cp ligand,
sometimes quite subtle, leads to significant changes in reactivity
as well as regio-, diastereo-, and chemoselectivity.
The use of electron-deficient Cp ligands such as Cp*CF39 and
CpE10 in place of Cp*Rh(III) complexes increases reactivity for
the synthesis of dihydropyridines11 and indoles (eqs 1 and 2).12
Although the development of new cyclopentadienyl ligands
(CpX) for Rh(III)-catalysis undeniably has the potential to
impact the field, several questions remain. One can appreciate
the diversity of ligand substitution patterns ranging from
mono-, di-, to pentasubstituted cyclopentadienyls with distinct
electronic and steric properties. Subtle variations in CpX ligand
derivatives often have a sizable impact on reactivity and
selectivity for which the steric and electronic origins remain
unclear. Often, the attempts to establish a relationship between
the structure of a catalyst and its reactivity lead to a speculative
analysis.
Recently, Sigman and co-workers elegantly displayed multi-
variate linear regression analysis to quantitatively describe the
interplay between the structure of a catalyst and selectivity.20
Importantly, this approach possesses predictive power to assist
further catalyst design. Inspired by Sigman’s work, we
undertook a comprehensive study to shed light on cyclo-
pentadienyl ligand effects in Rh(III) catalysis. We began by
synthesizing a large collection of systematically perturbed
CpXRh(III) complexes. Molecular descriptors that quantify
these structural changes were then identified and measured. We
investigated the performance of these catalysts in model
reactions to probe the interplay between the structure and
reactivity of CpXRh(III) complexes in C−H activation
reactions. Subsequently, multivariate regression was utilized to
establish correlations between structural properties and
reactivity/selectivity to determine the requirements for optimal
catalyst performance. In addition to the comprehensive and
instructive value of this study, we expect that this work could
serve the community as a starting point for the design of new
cyclopentadienyl-type ligands to uncover the full potential of
Rh(III) chemistry. Moreover, this approach is transposable to
other metals with the emergence of new promising method-
ologies employing Cp*Co(III)21 and Cp*Ir(III)22 catalysts in
C−H activation.
RESULTS AND DISCUSSION
■
Ligand Library. At the outset of our study, we synthesized
a library of 22 dimeric Rh(III) catalysts bearing various
cyclopentadienyl-type ligands (Rh1−22, Figure 2). Among this
Figure 1. Cyclopentadienyl-based ligands in Rh(III)-catalyzed C−H
activation reactions.
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2016−2044 and 1948−1984 cm−1 regions that are assigned as
the antisymmetric and symmetric stretching modes, respec-
tively. We anticipated that more electron-donating CpX ligands
increase the electron density at the metal center and
consequently increase the intensity of back-donation into the
CO π* orbital, which in turn weakens and lengthens the CO
bond. This lowers the CO stretching frequency. Conversely,
more electron-withdrawing CpX ligands give Rh′ complexes
with higher CO stretching frequencies.
Antisymmetric CO stretching frequencies (νA) are depicted
in Figure 3A. A closer analysis shows that penta-alkylated
complexes Rh′1−4 and Rh′11 possess similar electronic
properties (νA in the range 2016−2017 cm−1) and are among
the most electron-rich complexes in the collection of Rh(III)
catalysts. In accordance with the electronic nature of
substituents on the aryl ring of ligands of the type C5Me4Ar
in complexes Rh′5−7, the electron density at the metal center
increases in the order of Rh′6 < Rh′5 < Rh′7 (2017 < 2019 <
2026 cm−1). However, for the perfluorinated complex Rh′8,
this trend is no longer valid. Indeed, according to the scale, its
electron density is closer to that of Rh′6 (2016 vs 2017 cm−1),
which is counterintuitive considering the high electronegativity
of fluorine atoms. The presence of a strongly electron-
withdrawing substituent such as CF3 on the Cp ring decreases
the electron density substantially (Rh′10, 2040 cm−1). When a
TMS group is attached to the Cp ring (Rh′9, 2019 cm−1), the
Rh(I) complex is surprisingly more electron-deficient than the
parent Rh′1 (2016 cm−1). Notably, the heptamethylindenyl
Rh(I) complex (Rh′22, 2021 cm−1) is markedly more electron-
deficient than the parent Cp*Rh(I) complex (Rh′1, 2016
cm−1).
Figure 2. Structures of dimeric [CpXRhCl2]2 complexes used in this
study.
set of ligands, several had been described previously by us
(Rh2−3,19 Rh7,8h Rh10−11,9,19 and Rh20−2215,18,46b) and
others (Rh1310 and Rh19,17 Figure 1). Nevertheless, we
became restricted by the narrow number of CpXRh(III)
derivatives available in the literature. To overcome this
limitation, we undertook the synthesis of a new collection of
CpXRh(III) complexes (Rh4−6, Rh8−9, and Rh14−18).
We prepared cyclopentadienyl derivatives with different
substitution patterns ranging from mono-, di-, tri, tetra-, to
pentasubstituted with the goal to cover the widest spectrum of
steric and electronic properties accessible for these Rh(III)
complexes. X-ray crystal structures were obtained for most of
these [CpXRhCl2]2 complexes (see the Supporting Informa-
tion).23 Similarly to the Cp*Rh(III) complex (Rh1), the plane
of the various CpX rings is perpendicular to the Rh−
Cp(centroid) axis, with negligible tilting. With a set of Rh(III)
catalysts in hand, we initiated a systematic experimental and
computational study of their steric and electronic properties.
Such analysis will serve as a foundation for understanding
ligand effects in Rh(III)-catalyzed reactions.
Evaluating Electronic Effects. As a starting point, we
sought to define a set of quantitative electronic descriptors for
our library of CpXRh(III) complexes, bearing in mind that a
rigorous delineation between the steric and electronic proper-
ties of a ligand is challenging, because both are inherently
related (vide infra). For our purposes, six approaches were
chosen to quantify ligand electronic properties. The data are
summarized in Table 1.
IR Stretching Frequencies. Classically, the Tolman
electronic parameter (TEP) is used to probe the electron-
donating or -withdrawing ability of phosphine ligands
coordinated to a metal center.24 This concept was later
extended to characterize CpX ligands on various transition
metals.25 Thus, we sought to measure TEP values for
[CpXRh(CO)2] complexes (Rh′) as an indicator of electronic
density at the metal center, with the assumption that the trends
observed for low-valent Rh(I) would be transposable to high-
valent Rh(III) complexes.26 To do so, Rh(I)−carbonyl
complexes (Rh′) were synthesized from their corresponding
[CpXRhCl2]2 congeners (Rh). The frequencies of symmetric
(νS) and antisymmetric (νA) stretching bands of the CO ligands
on Rh(I) were then acquired by IR spectroscopy (Table 1). For
each Rh′ complex, one can observe two strong bands in the
31P NMR Data. The phosphorus atom of a coordinated
phosphine ligand is directly influenced by the electronic
environment in the vicinity of the metal center.27 As an
alternative approach for electronic parametrization of CpX
ligands, we converted the dimeric [CpXRhCl2]2 complexes
(Rh) into their corresponding monomeric triethylphosphite
adducts ([CpXRhP(OEt)3Cl2], Rh″). We then used 31P NMR
to measure the chemical shift of the phosphorus nucleus (δP)
and the coupling constant between phosphorus and rhodium
(JRh−P). The collected data for 31P chemical shifts (δP) was
compiled to construct an empirical scale of electronic
properties of CpX ligands (Figure 3B). As can be seen, in the
case of penta-alkylated complexes Rh″1−4 and Rh″11, the 31P
chemical shift decreases as the electron-donating ability of the
CpX ligand increases. This observation is consistent with
stronger shielding of the phosphorus nucleus in more electron-
rich Rh(III) complexes. On the other hand, complexes Rh″5−7
that bear a phenyl substituent no longer follow this trend. In
the case of mono- (Rh″21) and disubstituted (Rh″20)
complexes, the small differences in δP values do not account
for the expected decrease in electron density. Complexes such
as Rh″10 and Rh″13 that are among the most electron-
deficient according to the νA scale are found to be the most
deshielded on the δP scale.
Redox Potentials. Next, we measured the redox potentials
of Rh complexes reasoning that they would be influenced by
the electronic properties of the CpX ligands. This parameter
also benefits by avoiding a derivatization of the parent
complexes (introduction of ancillary ligand). To perform the
28
study, half-potentials of reduction (E1/2
)
of Rh1−22 were
measured by square-wave voltammetry. The results are
summarized in Table 1. Two irreversible one-electron
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Table 1. Electronic Parameters for CpXRh(III) Complexes
a
b
c
31P NMR of [CpXRhP(OEt)3Cl2] complexes (Rh″) in CD2Cl2. IR spectroscopy on [CpXRh(CO)2] complexes in C6D6 (Rh′). Measured by
square wave voltammetry on [CpXRhCl2]2 complexes (Rh) relative to Cp2Fe+/Cp2Fe. Reported E1/2 values represent the average of two
d
e
experiments. Calculated from atomic Natural Population Analysis (NPA). Gauge-Including Atomic Orbital (GIAO) calculated isotropic 103Rh
nuclear shielding tensors.
reductions are observed, corresponding to Rh(III/II) and
Rh(II/I) redox couples. The results for the Rh(III/II) couple
are ordered and presented in Figure 3C. The redox scale
displays a more pronounced intuitive character when compared
to the νA and δP scales. Accordingly, penta-alkylated complexes
Rh1−4 and Rh11 display the lowest reduction potentials
(−1.34 to −1.30 V), which places them among the most
electron-rich ones. The presence of electron-withdrawing
substituents on the CpX ring affects the redox potentials in a
predictable manner, increasing in the order of Rh1 < Rh6 <
Rh5 < Rh7 < Rh8 < Rh10 < Rh13 (−1.34 to −0.84 V).
Decreasing the degree of alkylation of the CpX ring leads to
more easily reducible complexes (Rh1 < Rh14 < Rh18 < Rh19
< Rh20 < Rh21, −1.34 to −1.01 V), consistent with a decrease
in electron density. Finally, the heptamethylindenyl complex
Rh22 (−1.07 V) is significantly more electron-deficient than
the parent Cp* complex (Rh1, −1.34 V).
Computed Electronic Parameters. Density functional
theory (DFT) calculations were carried out to investigate the
electronic characteristics of the CpXRh(III) complexes. Geo-
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Figure 3. A selection of ground-state molecular descriptors used for electronic parametrization. (A) Antisymmetric CO stretching frequencies (νA).
III/II
(B) Experimental 31P chemical shifts (δP). (C) Redox half-potentials for the Rh(III/II) couple (E ). (D) Computed 103Rh isotropic shielding
1/2
tensors (σRh).
metries of all 22 rhodium dimers (Rh) were optimized at the
wB97XD/def2-SVPP level of theory, which was found to be the
most accurate of several functionals surveyed (for benchmark-
ing studies see the Supporting Information).29 The electron
density at the metal center in each complex was evaluated in
terms of the Natural Population Analysis (NPA) atomic
charges at Rh (qRh) and the isotropic 103Rh nuclear shielding
tensor (σRh). These properties are computed directly for each
Rh center and are therefore complementary to our
experimental parameters, which capture changes in electron
density at the metal center indirectly.30 A scale of σRh for
complexes Rh1−22 is shown in Figure 3D. In this case, the
most deshielded (i.e., more negative σRh) Rh centers are found
Evaluating Steric Effects. Steric parametrization employ-
ing experimentally and computationally determined molecular
descriptors has been studied for over 60 years.31−35 The
evaluation of steric properties of CpX ligands in rhodium
complexes was accomplished by using two approaches (Table
2).
Tolman Cone Angles (TCA). Steric bulk has been
quantified extensively for phosphine ligands using the approach
initially developed by Tolman.36 Inspired by the TCA
definition for dissymmetrically substituted phosphines, we
adapted the formalism to substituted CpX ligands (Figure
4A).37 Accordingly, angles αi between the vector defined by the
Rh−Cp(centroid) axis, and the vectors tangential to the
outermost atomic sphere of each substituent Ri are measured in
DFT-optimized structures (see the Supporting Information).
Assuming that free rotation of the CpX ring around the Rh−
Cp(centroid) axis takes place in solution, each substituent Ri
occupies one of the five ring positions only 20% of the time.
Thus, for a Cp ring with two possible substituent types RS
(small) and RL (large), the average cone angle is given by eq 6
where n is the number of RL substituents. Considering Corey−
Pauling−Koltun (CPK) models,38 angles αS and αL have to be
corrected to account for atomic van der Waals (VdW) radii
using eq 7 to give half-vertex angles θi for each substituent type.
With VdW radii taken from Bondi’s compilation,39 the cone
angles are indexed as “Bondi” ΘB. When atomic radii are taken
in Rh8, Rh13, and Rh21, which are also the most electron-
III/II
1/2
deficient complexes according to the νA and E
scales. The
103Rh shielding tensors of mono- (Rh21), di- (Rh19 and
Rh20), tri- (Rh18), tetra- (Rh14), and penta-alkylated (Rh1−4
and Rh11) complexes are less negative, showing increased
shielding with more substituted CpX rings. Increasing the
number of phenyl groups on the ligand leads to greater
deshielding of the 103Rh nucleus (Rh5-7 vs Rh17 and Rh12),
suggesting a through-space π-effect in ligands of the type
C5Me4Ar. According to the computed σRh scale, the
heptamethylindenyl complex Rh22 contains the most elec-
tron-deficient Rh center.
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Table 2. Steric Parameters for CpXRh(III) Complexes
a
All parameters computed from wB97XD/def2-SVPP optimized structures. Using Bondi atomic van der Waals radii, cone angles as defined in Figure
b
c
8A. Using CPK atomic radii as used in Verloop’s original Sterimol program, lengths as defined in Figure 4B. See the Supporting Information for
details.
from Verloop’s original parameters (vide infra), the cone angles
are indexed as “Sterimol” (ΘS). While Bondi defined a single
radius for each element, those used in Sterimol take into
account formal hybridization and functional group. For
example, nine different atom types are used to describe carbon.
Elaborating on the above definitions, cone angles for all 22
Rh complexes were determined from DFT-optimized geo-
metries (Table 2). The results for averaged Bondi cone angles
(ΘB) are collated in Figure 5A.
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Figure 4. Steric descriptors of CpX ligands. (A) Definition of the
Tolman cone angle for a CpX ligand with two substituent types (RS
and RL). αi = angle between the vector defined by the Rh−
Cp(centroid) axis and the vector tangential to the outermost atomic
sphere (Hmax) of each substituent Ri; di = distance between the Rh
atom and the center of Hmax; θi = half-vertex angle to Hmax corrected
for atomic VdW radius; rH = VdW radius of Hmax; Θ = averaged cone
angle for the entire ligand. (B) Definition of Sterimol parameters. All
angles and distances are measured on CPK models. B1 = minimum
width of the CpX ligand perpendicular to the Rh−Cp(centroid) axis;
B5 = maximum width; L = Rh−Cp(centroid) distance.
According to the ΘB scale, catalysts Rh12, Rh13, Rh15,
Rh17, and Rh20 give rise to the largest cones, ranging from
194.2° to 197.3°. In the case of complexes Rh12 and Rh20, this
large value can be explained by the presence of two large tert-
butyl or phenyl groups tethered to the Cp ring. For Rh15 and
Rh17, one large tert-butyl or phenyl group in conjunction with
a fused cyclohexyl ring40 increases the overall bulkiness of the
catalyst. The very large cone angle calculated for complex Rh13
(194.5°) is at first sight counterintuitive. It can be rationalized
by considering the lowest-energy conformation of Rh13, where
the carbonyl groups are slightly twisted and point the ethyl
chains down toward the metal. The presence of outliers such as
Rh13 reveals the limitations of the convention used to calculate
Tolman cone angles. On the other hand, the size of the cone
decreases when the degree of substitution of the Cp ring
diminishes, in the order of Rh1 > Rh21 > Rh14 (178.7−
176.4°) for cyclopentadienyl ligands, and Rh18 > Rh19 (185.2°
vs 184.6°) for cyclohexyl-fused ring systems. In contrast, the
heptamethylindenyl complex Rh22 has a relatively small ΘB
cone angle (183.4°). While averaged cone angles seem to
adequately describe the total isotropic volume of CpX ligands,
the subtleties of their shape are not accounted for by the ΘB
value alone. We reasoned that a refined picture would be
achieved by disassembling this value into subparameters. In an
Figure 5. A selection of geometrical descriptors used for steric
parametrization. (A) Averaged Bondi cone angles (ΘB). (B) Difference
of cone angles Θmax − Θmin. (C) Sterimol B5 − B1 values.
attempt to do so, the minimum (ΘB,min) and maximum (ΘB,max
)
Bondi cone angles, defined as the half-vertex angles to the
smallest and largest substituents on the CpX ring, respectively,
were extracted. The value Θmax − Θmin was then calculated to
underline molecular anisotropy. A relative scale is presented in
Figure 5B. Accordingly, complexes Rh7, Rh9, and Rh13 have
the largest cone angle anisotropy, while Rh1, Rh4, and Rh14
are more uniformly shaped. Interestingly, complex Rh20, which
gives the highest regioselectivity in the 1-decene insertion
reaction (vide infra), displays a pronounced anisotropic
character. On the other hand, complex Rh22 that leads to
one of the highest diastereoselectivities for cyclopropene
insertion (vide infra) shows very small cone angle anisotropy.
Further elaborating on the idea of operating with steric
subparameters, a different set of multidimensional steric
descriptors was investigated.
Sterimol Parameters. Sterimol parameters are molecular
steric descriptors developed for use in quantitative structure−
activity relationships (QSAR).41 They have recently found
application in asymmetric catalysis, correlating more strongly
than empirically determined (isotropic) Charton or Taft
parameters in several linear and nonlinear structure−selectivity
relationships.42 Verloop developed the Sterimol program43 to
describe the anisotropic nature of a substituent’s steric demands
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through a combination of subparameters, three of which remain
in current use: two width parameters (B1 and B5) and a length
parameter (L). In the original (Fortran) implementation,
Sterimol parameters are generated from a list of atom types
(based on element, hybridization, and functional group) and
dihedral angles from which the atomic coordinates are
generated using tabulated bond lengths and angles, while the
molecule surface is defined by CPK atomic radii. However, in
principle, it is also possible to define molecular coordinates
using, for example, DFT-optimization along with a set of
atomic radii. We first established that for a set of given
structures we generated identical Sterimol parameters using
either the original Fortran77 code, MMP+, or an in-house
Python implementation. To define the steric profile of CpX
ligands, a simple modification of Verloop’s original approach
was required. DFT-optimized geometries were used where the
width subparameters (B1 and B5) are calculated according to
the CPK surface of the CpX ring when viewed along the Rh−
Cp(centroid) axis (Figure 4B). The L subparameter is
calculated by measuring the length of the system along the
Rh−Cp(centroid) axis. The B1 and B5 subparameters are
calculated in the standard way by taking the perpendicular
distance from the axis to the tangential, peripheral plane nearest
to, and furthest from, the L axis. The value B5 − B1 constitutes a
simplified representation of anisotropy of the CpX ligand in the
plane of the ring (Figure 5C). As can be seen, disc-shaped CpX
ligands like those found in complexes Rh1, Rh10, and Rh14
give rise to small B5 − B1 values, and thus little anisotropy.
Ligands with a more elliptical shape, like those in complexes
Rh6−8 and Rh16, lead to larger B5 − B1 values, and thus more
pronounced anisotropic character.44 However, the Sterimol
approach does not account for the high anisotropy of Rh20
that was observed with Θmax − Θmin values. This inconsistency
stems from the definition of the B5 subparameter. If a second
substituent that is at least as large as the one giving rise to B5 is
present on the Cp ring, its influence on the shape of the ligand
will be ignored. This issue also occurs in complexes Rh12,
Rh13, and Rh17 bearing two identically large substituents
attached to the Cp ring. Additionally, one should recognize that
the Sterimol approach only considers dimensions inside the
plane of the Cp ring. Arguably, more interesting for studying
our model reactions is the out-of-plane steric bulk in the
vicinity of the metal because substrate binding occurs at this
site. Cone angle anisotropy (Θmax − Θmin, vide supra) might be
more suited for this purpose. Nevertheless, we believe that
when associated together, cone angles and Sterimol parameters
might complement each other and provide a more complete
description of the steric environment around the CpX ligand.
Overall, the above analysis reveals that small changes in the
structure of the CpX ligand can readily affect the steric and
electronic properties of the CpXRh(III) catalyst. We were then
interested to study the impact of these catalyst perturbations on
reactivity and selectivity in model reactions.
Figure 6. Model reactions. (A) Rh(III)-catalyzed annulation reaction
between hydroxamic acid derivatives and alkenes for the synthesis of
3,4-dihydroisoquinolones. (B) General mechanism.
the nature of the π-component, the migratory insertion step
becomes selectivity-determining. Regio- and diastereoselectivity
values represent relative rate measurements
[major]
(
ΔΔG⧧ = −RT ln
). Therefore, selectivity and rate
[minor]
(
)
data sets can provide key insights regarding specific molecular
interactions responsible for selectivity as well as catalytic
activity. In this regard, two CpXRh(III)-catalyzed reactions
previously developed in our group were selected as models
(vide infra). We then systematically measured the regio- and
diastereoselectivities as well as the catalytic activity obtainable
with various Rh(III) catalysts (Rh1−22). Subsequently, the
obtained data sets were correlated to experimental and
computational ground-state catalyst properties, with the goal
of gaining insight into the molecular features responsible for
optimal catalyst performance.
1-Decene Insertion. As previously discussed, the regiose-
lectivity of Rh(III)-catalyzed insertion of 1-decene 2a into the
C−H bond of O-pivaloyl benzhydroxamic acid 1 is strongly
dependent on the nature of the CpX ligand on rhodium leading
to a mixture of regioisomers 3a and 3a′ (Figure 7A).19 On the
basis of catalyst performance in the title reaction, several trends
emerge (Figure 7B). For ligands of the type C5Me4R, increasing
the steric bulk of the R substituent (see the ΘB scale, R = t-Bu >
Ph > Cy > i-Pr > Me) leads to a higher regioisomer ratio (Rh11
> Rh5 > Rh3 > Rh2 > Rh1, 8.7−2.4:1 rr). Considering ligands
of the type C5Me4Ar, more electron-rich aromatic rings
III/II
1/2
according to E
values (Ar = H > 4-OMe > 3,5-bis-CF3 >
Choice of Model Reactions. Rh(III)-catalyzed insertion of
alkenes into benzohydroxamic acid derivatives to produce
dihydroisoquinolinones has been investigated extensively
(Figure 6A).16,19,45 A unified mechanism can be drawn (Figure
6B). It has been shown by kinetic isotope effect experiments
that C−H insertion is often the turnover-limiting step in these
reactions. With the assumption that the equilibrium constant
for active catalyst generation is large enough (k1/k−1 > k2), the
overall rate of reaction can be approximated by the rate of the
C−H insertion step alone (k2 ≈ kobs). Moreover, depending on
C6F5, Figure 4C) lead to better regioselectivity accordingly
(Rh5 > Rh6 > Rh7 > Rh8, 5.3−3.0:1 rr). For structurally
related ligands of the type C5Me4R, more electron-withdrawing
III/II
1/2
substituents according to the E
scale (R = CF3 > Bn > Me)
increase the regioselectivity of the reaction (Rh10 > Rh4 >
Rh1, 3.7−2.4:1 rr). The results are conveniently visualized on a
free energy scale (Figure 7C).
Cyclopropene Insertion. The diastereoselectivity of cyclo-
propene insertion into the C−H bond of O-pivaloyl
benzhydroxamate 1 is strongly dependent on the nature of
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placed on the convex face of the molecule (Figure 8A). When
analyzing the diastereomeric ratios obtained with different
CpXRh(III) complexes, few similarities can be drawn with the
trends observed for the regioselectivity of 1-decene insertion
(Figure 7B). Notably, with ligands of the type C5Me4R,
increasing the steric bulk of the R substituent (see ΘB values for
R = t-Bu > Ph > Cy > i-Pr > Me) leads to higher diastereomeric
induction (Rh11 > Rh5 > Rh3 > Rh2 > Rh1, 9.6−5.8:1 dr).
Additionally, increasing the degree of substitution of the Cp
ring yields a more selective reaction (Rh1 > Rh14 > Rh18 >
Rh19, 5.8−0.9:1 dr), but complex Rh21 falls out of the general
trend (Rh21 > Rh19, 1.1:1 vs 0.9:1 dr). Interestingly, while
performing well in the previous reaction, Rh20 (12:1 rr, Figure
7B) fails to produce high diastereoselectivity in the present
reaction (5.0:1 dr, Figure 8B). The opposite holds true for the
heptamethylindenyl ligand (Rh22) that gives 15.2:1 dr for
cyclopropene insertion, in contrast to 1.1:1 rr for 1-decene
insertion. On the other hand, complex Rh12 displays equally
good performances in both transformations (10.1:1 rr and
19.5:1 dr).47 A scale for diastereoselectivity is depicted below
(Figure 8C).
Kinetic Measurements. To gain further insight into the
influence of ligand structure on the outcome of Rh(III)-
catalyzed reactions, the rate of 1-decene (2a) insertion into O-
pivaloyl benzhydroxamate (1) was monitored for catalysts
Figure 7. Influence of CpX ligand structure on the regioselectivity of 1-
decene insertion. (A) Coupling of O-pivaloyl benzhydroxamate 1 and
1-decene 2a. (B) Regioisomer ratios obtained with different
CpXRh(III) complexes. (C) Scale of regioisomer ratios in the form
of activation energy differences (ΔΔG⧧).
1
Rh1−22 by H NMR spectroscopy (Figure 9). The recorded
concentration of products (3a+3a′) versus time profiles were
readily fitted to single-exponential curves, suggesting first-order
behavior in all cases (Figure 9A). This implies that C−H
activation is turnover limiting regardless of the nature of the
catalyst. The extracted rate constants are presented in Figure
9C.
the CpX ligand on the Rh(III) catalyst (Figure 8).46 Indeed,
when a cyclopropene bearing a prostereogenic center such as
2b is used, two diastereomeric products arise (3b and 3b′). In
the major diastereomer 3b, the bulkier phenyl substituent is
For CpX ligands of the type C5Me4R, the reaction is faster
when a weakly electron-withdrawing substituent is present.
Accordingly, Rh4 (R = Bn, E
= −1.30 V, kobs = 7.28 × 10−4
III/II
1/2
s−1) is faster than Rh1 (R = Me, E
= −1.34 V, kobs = 6.28 ×
III/II
1/2
III/II
1/2
10−4 s−1), whereas Rh6−8 are all three faster than Rh5 (E
=
−1.24, −1.25, −1.18, −1.09 vs −1.25 V; kobs = (5.09−4.38 vs
3.30) × 10−4 s−1). Substituents with large ΘB values slow the
reaction; for example, the rate decreases in the order of Rh1 >
Rh2 > Rh5 ≫ Rh11 ((6.28−2.92) × 10−4 s−1). Very bulky CpX
ligands, such as those found in complexes Rh11, Rh12, and
Rh20, lead to the slowest reaction rates (2.92, 3.48, 0.66 × 10−4
s−1, respectively). When CpX ligands contain strongly electron-
withdrawing groups such as CF3 and CO2Et (Rh10 and Rh13,
respectively), the rate decreases significantly. Decreasing the
number of substituents on the Cp ring tends to slow the
reaction considerably (Rh1 ≫ Rh14, Rh21, 6.28 vs 2.09, 2.31
× 10−4 s−1). An exception are cyclohexyl-fused ligands like
those in complexes Rh15−19 for which the rate is somewhat
restored, especially when additional substituents on the Cp ring
are present. Indeed, complex Rh17 gives rise to the fastest
catalytic activity in the series (8.19 × 10−4 s−1). The kinetic data
are arranged according to their respective free energies in
Figure 9C.
Having defined the model reactions and the parameter space
accordingly, we performed multivariate linear regression to
correlate the relevant catalyst parameters to regioselectivity,
diastereoselectivity, and rate.
Correlating Catalyst-Based Parameters to Selectivity
and Rate of Model Reactions. Ideally, a single electronic
and/or a single steric parameter will capture the covariance of
catalyst structure with reaction outcome, as in a Hammett or
Figure 8. Influence of CpX ligand structure on the diastereoselectivity
of cyclopropene insertion. (A) Coupling of benzhydroxamic acid
derivative 1 and cyclopropene 2b. (B) Diastereomer ratios obtained
with different CpXRh(III) complexes. (C) Scale of diastereomer ratios
in the form of activation energy differences (ΔΔG⧧).
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Figure 9. Catalytic activity of CpXRh(III) complexes for 1-decene (2a) insertion. (A) Normalized concentration of 3a + 3a′ vs time. (B) Extracted
first-order rate constants (kobs). (C) Scale of rate constants in the form of activation energy differences (ΔΔG⧧).
Taft analysis. Yet often, the molecular diversity of a large
catalyst library cannot be distilled to one or two dimensions.
Instead, an ensemble of quantitative molecular descriptors is
usually required to account for all of the subtleties of chemical
structure. Moreover, the steric and electronic components of
these parameters cannot be completely decoupled from one
another, except in rare cases.34,35,48 Recently, stepwise linear
regression algorithms found widespread utility in handling the
typically large number of parameters involved in multivariate
free energy relationships.21,36,37
With the appropriate library of CpXRh(III) complexes in
hand (Figure 2), the selectivity and reactivity data sets of model
reactions were used to produce correlations between the
experimental values and catalyst-based molecular descriptors.
The parameter set consisted of 15 elements, including 31P (δP)
chemical shifts, computed 103Rh shielding tensors (σRh), Rh−P
coupling constants (JRh−P), IR stretching frequencies (νS and
III/II
1/2
II/I
νA), redox potentials (E
and E ), natural charge on Rh
1/2
atom (qRh), Sterimol parameters (L, B1, B5), and cone angles
(ΘS, ΘB, ΘB,max, and ΘB,min). We first constructed global
regression models (GMs) for regioselectivity, diastereoselectiv-
ity, and rate, each composed of all 22 catalysts and employing
only linear combinations of parameters from the set. In cases
where a large number of parameters had to be used to obtain a
good fit, interaction terms were included to reduce the number
of elements of the correlation equations. GMs were para-
metrized with either a principal (training set 1, or T1) or two
secondary (T2 and T3) training sets (gray circles, Figures 10,
11, and 13). T1 is comprised of 18 structures, Rh1−7, Rh9,
Rh10, Rh13, Rh14, and Rh16−22. In T2, complex Rh22 is
replaced by Rh8, while in T3, complex Rh13 is replaced by
Rh8 and Rh15 is replaced by Rh4. The CpXRh(III) complexes
selected for the training sets cover the spectrum of regio- and
diastereoselectivities representative of the different substitution
patterns of CpX ligands. The remaining structures were left out
for the validation sets (red triangles, Figures 10, 11, and 13). To
evaluate the statistical accuracy of all regression models, we
graphically represented predicted versus measured ΔΔG⧧,
which is the free energy difference, in kcal mol−1, between the
Figure 10. Mathematical correlations of normalized catalyst
parameters to regioselectivity (ΔΔG⧧) for the 1-decene insertion
reaction. (A) Global model for Rh1−22 with training set I. (B) Global
model for Rh1−22, including two interaction terms (δP·νS and νS·ΘB).
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regio- or diastereomeric transition states leading to isomeric
products.
Correlating Regioselectivity. Our purpose was to
produce multivariate correlations to describe the origins of
regioselectivity in the 1-decene insertion reaction (Figure 10).
When employing T1 as the training set, a statistically robust
regression model was obtained (R2 = 0.97, Figure 10A),
composed of three “electronic” terms, 31P chemical shifts (δP),
antisymmetric CO stretching frequencies (νA), and 103Rh
shielding tensor (σRh), as well as three “steric” components, L
parameters, and Sterimol and Bondi cone angles (ΘS and ΘB).
The regioselectivity model demonstrates an overall good
agreement between predicted ΔΔG⧧ values and experimental
data. The predictive capability of this regression model was
confirmed through external validation with complexes Rh8,
Rh11, Rh12, and Rh15, as well as a Q2 value test.49 As the
relationships in Figure 10 are normalized equations, the
magnitude of the coefficients gives insight on the relative
influence of each parameter on selectivity. Hence, the
“electronic” parameters δP, νA, and σRh have the largest
coefficients (0.46, 0.31, and 0.28, respectively) and, con-
sequently, an important influence on regioselectivity. On the
other hand, the Sterimol parameter L gives a small steric
contribution (0.11). It is interesting to note that the relative
weights of ΘS and ΘB are similar in magnitude but opposite in
sign (−1.31 vs +1.44), and overweight all other parameters in
the equation. Such behavior is commonly observed when alike
parameter couples are used in the correlation (e.g., νS and νA,
III/II
1/2
II/I
1/2
E
and E , or ΘS and ΘB). Additionally, pronounced
covariance between these couples is observed (see the
Supporting Information). We propose that the regression
algorithm couples ΘS and ΘB together in form of a weighted
difference, which gives rise to a crossed parameter (aΘS−bΘB)
with increased correlative power. This purely mathematical
adjustment compensates for the deficiency of averaged cone
angles to account for the caveats of steric structure when taken
separately.
In an oversimplified analysis, assuming perfect separation of
electronic and steric components for each parameter, the
percent contribution of electronic character is estimated at 81%
by using eq 8:
∑
∑
elec |ci|
tot |cj|
% electronic character =
(8)
where ci are the relative weights of “electronic” parameters, and
cj are the contributions from “steric” parameters. For coupled
parameters like ΘS and ΘB, the difference between coefficients
is taken to measure the relative weight of the entire term. Such
a high electronic component for the regioselectivity model
seems at first glance counterintuitive. In our opinion, this is
possible because some of the employed “electronic” parameters
like δP and JRh−P already have a significant steric component
embedded into them.
While our linear GM has the advantage of incorporating the
entire set of catalysts into the correlation, the complexity of the
resultant equation (six linear terms) renders its interpretation
challenging. To reduce the number of parameters needed to
accurately describe regioselectivity, interaction terms were
included into the regression process (Figure 10B). While
keeping the statistical quality at the same level (R2 = 0.93), the
number of required parameters for the interaction model is
reduced 2-fold, leaving three linear terms (δP, νS, and ΘB) and
Figure 11. Mathematical correlations of normalized catalyst
parameters to diastereoselectivity (ΔΔG⧧) for the cyclopropene
insertion reaction. (A) Global model for Rh1−22 with training set I.
(B) Global model for Rh1−22 with training set II. (C) Global model
for Rh1−22 with training set III.
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Figure 12. Optimized exo-trans transition structures for cyclopropene insertion leading to the major diastereomer (from left to right, Rh1, Rh12,
Rh22). Metal-centroid distance and ligand slippage parameter, Δ, shown in Å.
two interaction terms, δP·νS and νS·ΘB. Interestingly, both
interaction terms contain the electronic νS parameter, but, while
in the first term the frequency interacts with another electronic
term (δP), in the second one an interaction with a steric
parameter (ΘB) is observed. The larger coefficient for the latter
(0.08 vs 0.18) supports the empirical observation of an
interplay between steric and electronic properties of the catalyst
when describing regioselectivity.
suitable model was indeed found (R2 = 0.98), making use of 31P
chemical shifts (δP), 103Rh shielding tensors (σRh), Rh(II/I)
redox potentials (E1II//2I), Sterimol B1 parameters, and cone
angles (ΘS). Inclusion of the Sterimol B1 parameter is
consistent with substrate approach on the less hindered side
of the CpX ligand. When validating the model with an external
set of four members, Rh12 and Rh22 are both outliers this
time. This result suggests that the high diastereoselectivities
exhibited by catalysts Rh12 and Rh22 might arise from a
change in structure of the transition state.
Correlating Diastereoselectivity. We also searched for
correlations between the diastereoselectivity of cyclopropene
insertion (represented as ΔΔG⧧) and the molecular descriptors
of CpX ligands (Figure 11). When using T1 as the training set, a
regression model was found where the predicted selectivities
closely match those measured (R2 = 0.95, Figure 11A). Three
In contrast, this was not the case for complex Rh13. A linear
diastereoselectivity model was constructed with training set T3
that now includes Rh22, while Rh12 and Rh13 are left out of
the parametrization process (Figure 11C). This time, the
electronic parameters [31P chemical shifts (δP), NBO charges at
relevant parameters are 31P chemical shifts (δP), 103Rh−31P
III/II
1/2
II/I
1/2
rhodium (qRh), and Rh(III/II) redox potentials (E )] and
coupling constants (JRh−P), Rh(II/I) redox potentials (E ),
one steric parameter [the minimum Bondi cone angle (ΘB,min)]
are used in the correlation. A relatively large coefficient (0.10)
for ΘB,min is consistent with substrate approach on the less
hindered side of the CpX ligand in the diastereoselectivity-
imparting step.50 Further substantiation of the model was
provided through external validation with an additional set of
four CpXRh(III) complexes. As depicted in Figure 11A, the
model displays accurate predictive capability for estimating the
performance of complexes Rh8, Rh11, and Rh15, and a Q2
value of 0.91. However, complex Rh12 is obviously an outlier,
and its high diastereoselectivity (19.5:1 dr, ΔΔG⧧ = 1.76 kcal
mol−1, Figure 8B) is not well predicted (vide infra). Classically,
kinks or breaks in univariate correlations (e.g., Hammett plots)
are indicative of a change in mechanism for certain data set
members. In cases where multivariate correlations are applied,
this change could be identified through the presence of obvious
outliers.23 Building on these observations, we sought to explore
the possibility that a similar scenario took place with catalyst
Rh12 during the selectivity-determining step, and if a change in
mechanism is a requirement for high diastereomeric induction.
Furthermore, we questioned whether Rh12 is an isolated case
or such behavior could be extended to other members of the
catalyst library. We were particularly interested in the
performances of complexes Rh13 and Rh22 that give rise to
high diastereomer ratios (ΔΔG⧧ = 1.55 kcal mol−1, and ΔΔG⧧
= 1.61 kcal mol−1, respectively, Figure 8B). First, to check
whether Rh22 is an outlier, an additional regression model was
constructed with training set T2 that leaves both Rh12 and
Rh22 out of the parametrization process (Figure 11B). A
and Sterimol B1 parameters. Interestingly, while Rh12 is once
again an obvious outlier with this training set, the high
diastereomeric induction observed experimentally for Rh13 is
well described by the regression model (R2 = 0.95). This result
implies that, contrary to Rh12 and Rh22, (i) the performance
of catalyst Rh13 in the cyclopropene insertion reaction can be
predicted accurately on the basis of ground-state steric and
electronic properties of the catalyst, and (ii) that a mechanism
change is not a prerequisite for high diastereomeric induction.
It is important to note that the involvement of an alternate
mechanistic regime for complexes Rh12 and Rh22 is a unique
asset of the cyclopropene insertion reaction. Indeed, Rh12 and
Rh22 are no longer outliers in regioselectivity models.51
To shed light on the origins of the high levels of
diastereomeric induction displayed by catalysts Rh12 and
Rh22 in the cyclopropene insertion reaction, competing
transition structures (TSs) for the selectivity-determining
migratory insertion step48 were investigated computationally
(Figure 12).52 We found structural differences in the metal π-
coordination for these ligands, with Rh12 and Rh22 showing
larger variation than Rh1 in the five Rh−C distances associated
with the Rh−Cp interaction. These coordination modes thus
have greater (η3 + η2) character than (η5) Rh1. This effect,
which may be quantified in terms of a slippage parameter, Δ,
that reflects the average difference between longest and shortest
M−C distances (see the Supporting Information), is further
magnified in the insertion TSs. The anomalously high
diastereoselectivity obtained with the Ind* ligand (Rh22)
results from greater slippage in the favored insertion TS. The
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better slippage capability of the indenyl ligand toward a η3 + η2
bonding mode increases the diastereoselectivity by accom-
modating the methyl group in the exo-trans TS leading to the
major diastereomer. The stereoselectivity is enhanced due to
differences in ligand bonding mode and geometry. This effect is
most pronounced for complexes Rh12 and Rh22, which are
more prone to hapticity change. Because the ground-state
parameters that we use to construct our regression models
cannot describe this phenomenon adequately, it might explain
why the prediction fails for complexes Rh12 and Rh22. It is
interesting to see that, although the reactions involving 1-
decene or cyclopropene insertion are in essence very close, they
differ in terms of catalyst features to achieve high performances.
After studying the selectivity-imparting step of the model
reactions in detail, we investigated the catalytic activity of
complexes Rh1−22 next, by establishing multivariate correla-
tions between reaction rate and the set of catalyst-based
descriptors.
parameters, and an interaction term (qRh·E ). Similarly to the
II/I
1/2
behavior observed for cone angles Θ and ΘB in the
S
regioselectivity model, the relative weights of redox potentials
III/II
1/2
II/I
1/2
E
and E are similar in magnitude but opposite in sign
(−0.82 vs +0.43). This implies that mixing of these parameters
occurs during the regression process. Sterimol B1 parameter is
the only steric term in the equation and is consistent with
preferred substrate approach from the least hindered dimension
of the CpX ligand in the transition state of the C−H bond
cleavage step.53 Besides natural charges (qRh), the interaction
term makes use of redox potentials EI1I//2I, both being descriptors
of electron density at the metal center of Rh complexes. Two of
the highest reaction rates were observed for catalysts Rh4 and
Rh17 that have one of the most positive qRh and most negative
EI1I//2I values. While qRh numbers become more positive for more
electron-rich Rh(III) centers, E redox potentials become
more negative for more electron-rich complexes. Thus, the
interplay between qRh and E that display opposite tendencies
might account for the experimentally observed optimum of
catalyst electronegativity required to tune the reaction rate.
II/I
1/2
II/I
1/2
Correlating Reaction Rate. The C−H bond cleavage
event constitutes the rate-determining step in both model
reactions. We anticipated that a thorough analysis of this
particular step would shed light on the catalyst requirements for
high catalytic activity.
CONCLUSIONS
■
We have developed a series of quantitative linear regression
models that correlate reaction outcome such as selectivity and
reactivity to catalyst-based parameters. Our parametrization
process consisted of collecting experimental and computational
data for a large library of CpXRh(III) complexes. Two model
reactions were chosen to study correlations, one involving the
insertion of 1-decene and the other that of cyclopropene. The
influence of ligand structure on the first step (C−H activation)
was probed by measuring reaction rate, and the influence on
the second step (alkene insertion) was investigated by
measuring reaction regio- and diastereoselectivities. While
good correlations are seen for regioselectivity and rate, two
obvious outliers occurred between predicted and measured
ΔΔG⧧values for the diastereoselectivity model. DFT calcu-
lations have shown that the uncommonly high selectivity
displayed by the outliers can be explained by partial η5−η3
ligand slippage occurring in the transition state of the
selectivity-determining step. It is interesting to note that this
does not seem to take place during 1-decene insertion. Our
regression models show predictive capability, allowing for
determination of reaction selectivity and rate given a set of
descriptive parameters for a chosen ligand. We hope that this
study could serve as a starting point for the design of new
cyclopentadienyl-type ligands and uncover the full potential of
Rh(III) chemistry.
To initiate our study, the rate constants (kobs) shown in
Figure 9B were converted to their corresponding Gibbs free
energies of activation according to the Eyring equation
kX
(ΔΔG⧧ = −RT ln
). The regression model was then
(k )
0
parametrized with training set T1, leaving Rh8, Rh11, Rh12,
and Rh15 out for the validation set. While being strongly
correlative (R2 = 0.95) and possessing accurate predictive
capability (Q2 = 0.82), the linear rate model is complicated by
the large number of parameters it requires (eight terms) and
reveals little regarding the interactions at the origin of high
catalytic activity (see the Supporting Information). As depicted
in Figure 13, a statistically reliable interaction model (R2 =
0.93) was constructed by employing 103Rh−31P coupling
constants (JRh−P), natural charges at rhodium (qRh), Rh(III/
III/II
1/2
II/I
II) and Rh(II/I) redox potentials (E
and E ), Sterimol B1
1/2
ASSOCIATED CONTENT
* Supporting Information
The Supporting Information is available free of charge on the
ACS Publications website at DOI: 10.1021/jacs.6b11670.
■
S
X-ray crystallographic data (ZIP)
Experimental procedures, analytical data, NMR spectra,
and computational methods (PDF)
X-ray data (PDF)
Experimental and computational data (XLSX)
AUTHOR INFORMATION
■
Corresponding Authors
*robert.paton@chem.ox.ac.uk
*tr2504@columbia.edu
Figure 13. Mathematical correlation of normalized catalyst parameters
to catalytic activity (ΔΔG⧧) for the 1-decene insertion reaction. Global
II/I
1/2
model for Rh1−22 including an interaction term (qRh·E ).
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(16) Hyster, T. K.; Dalton, D. M.; Rovis, T. Chem. Sci. 2015, 6, 254.
(17) Wodrich, M. D.; Ye, B.; Gonthier, J. F.; Corminboeuf, C.;
Cramer, N. Chem. - Eur. J. 2014, 20, 15409.
(18) Piou, T.; Rovis, T. J. Am. Chem. Soc. 2014, 136, 11292.
(19) Piou, T.; Rovis, T. Nature 2015, 527, 86.
(20) For selected examples, see: (a) Harper, K. C.; Sigman, M. S.
Science 2011, 333, 1875. (b) Harper, K. C.; Vilardi, S. C.; Sigman, M.
S. J. Am. Chem. Soc. 2013, 135, 2482. (c) Milo, A.; Bess, E. N.; Sigman,
M. S. Nature 2014, 507, 210. (d) Bess, E. N.; DeLuca, R. J.; Tindall, D.
J.; Oderinde, M. S.; Roizen, J. L.; Du Bois, J.; Sigman, M. S. J. Am.
Chem. Soc. 2014, 136, 5783. (e) Bess, E. N.; Bischoff, A. J.; Sigman, M.
S. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, 14698. (f) Milo, A.; Neel, A.
J.; Toste, F. D.; Sigman, M. S. Science 2015, 347, 737. (g) Bess, E. N.;
Guptill, D. M.; Davies, H. M. L.; Sigman, M. S. Chem. Sci. 2015, 6,
3057. (h) Hickey, D. P.; Schiedler, D. A.; Matanovic, I.; Doan, P. V.;
Atanassov, P.; Minteer, S. D.; Sigman, M. S. J. Am. Chem. Soc. 2015,
137, 16179. (i) Mougel, V.; Santiago, C. B.; Zhizhko, P. A.; Bess, E. N.;
ORCID
Robert S. Paton: 0000-0002-0104-4166
Tomislav Rovis: 0000-0001-6287-8669
Author Contributions
⊥T.P. and F.R.-M. contributed equally to this work.
Author Contributions
#M.R.-M. and K.E.J. contributed equally to this work.
Notes
The authors declare no competing financial interest.
ACKNOWLEDGMENTS
■
We thank NIGMS (GM80442 to T.R.) for support, and
acknowledge the use of the EPSRC UK National Service for
Computational Chemistry Software (CHEM870 to R.S.P.) in
carrying out this work and the dirac cluster at the University of
Oxford (EP/L015722/1).
́
Varga, J.; Frater, G.; Sigman, M. S.; Coperet, C. J. Am. Chem. Soc.
2015, 137, 6699. (j) Neel, A. J.; Milo, A.; Sigman, M. S.; Toste, F. D. J.
Am. Chem. Soc. 2016, 138, 3863. For a recent review, see: (k) Sigman,
M. S.; Harper, K. C.; Bess, E. N.; Milo, A. Acc. Chem. Res. 2016, 49,
1292.
REFERENCES
■
(1) Wilkinson, G.; Gillard, R. D.; McCleverty, J. A. Comprehensive
Coordination Chemistry; Pergamon: New York, 1987; Vol. 2.
(2) Collman, J. P.; Hegedus, L. S.; Norton, J. R.; Finke, R. G.
Principles and Applications of Organotransition Metal Chemistry;
University Science Books: Mill Valley, CA, 1987; p 57.
(21) For pioneering work, see: (a) Yoshino, T.; Ikemoto, H.;
Matsunaga, S.; Kanai, M. Angew. Chem., Int. Ed. 2013, 52, 2207. For
recent examples, see: (b) Suzuki, Y.; Sun, B.; Sakata, K.; Yoshino, T.;
Matsunaga, S.; Kanai, M. Angew. Chem., Int. Ed. 2015, 54, 9944. (c) Li,
J.; Ackermann, L. Angew. Chem., Int. Ed. 2015, 54, 3635. (d) Hummel,
J. R.; Ellman, J. A. J. Am. Chem. Soc. 2015, 137, 490. (e) Yu, D.-G.;
Gensch, T.; de Azambuja, T.; Vasquez-Cespedes, S.; Glorius, F. J. Am.
Chem. Soc. 2014, 136, 17722.
(22) For a review, see: Shin, K.; Kim, H.; Chang, S. Acc. Chem. Res.
2015, 48, 1040.
(23) Single crystals for complexes Rh13 and Rh16 of adequate
quality were not obtained.
(3) For recent reviews, see: (a) Colby, D. A.; Bergman, R. G.; Ellman,
J. A. Chem. Rev. 2010, 110, 624. (b) Satoh, T.; Miura, M. Chem. - Eur.
J. 2010, 16, 11212. (c) Patureau, F. W.; Glorius, F. Angew. Chem., Int.
Ed. 2011, 50, 1977. (d) Patureau, F. W.; Wencel-Delord, J.; Glorius, F.
Aldrichimica Acta 2012, 45, 31. (e) Song, G.; Wang, F.; Li, X. Chem.
Soc. Rev. 2012, 41, 3651. (f) Chiba, S. Chem. Lett. 2012, 41, 1554.
(4) (a) Ueura, K.; Satoh, T.; Miura, M. Org. Lett. 2007, 9, 1407.
(b) Umeda, N.; Tsurugo, H.; Satoh, T.; Miura, M. Angew. Chem., Int.
Ed. 2008, 47, 4019. (c) Fukutani, T.; Umeda, N.; Hirano, K.; Satoh,
T.; Miura, M. Chem. Commun. 2009, 5141. (d) Morimoto, K.; Hirano,
K.; Satoh, T.; Miura, M. Org. Lett. 2010, 12, 2068. (sd) Mochida, S.;
Umeda, N.; Hirano, K.; Satoh, T.; Miura, M. Chem. Lett. 2010, 39, 744.
(5) Stuart, D. R.; Bertrand-Laperle, M.; Burgess, K. M. N.; Fagnou, K.
J. Am. Chem. Soc. 2008, 130, 16474.
(24) Tolman, C. A. Chem. Rev. 1977, 77, 313.
(25) (a) Shen, J.; Basolo, F.; Nombel, P.; Lugan, N.; Lavigne, G.
Inorg. Chem. 1996, 35, 755. (b) du Plooy, K. E.; du Toit, J.; Levendis,
D. C.; Coville, N. J. J. Organomet. Chem. 1996, 508, 231. (c) du Plooy,
K. E.; Ford, T. A.; Coville, N. J. J. Organomet. Chem. 1999, 579, 348.
(26) (a) Rerek, M. E.; Basolo, F. J. Am. Chem. Soc. 1984, 106, 5908.
(b) Cheong, M.; Basolo, F. Organometallics 1988, 7, 2041.
(27) (a) Lyatifov, I. R.; Gulieva, G. I.; Babin, V. N.; Materikova, R. B.;
Petrovskii, P. V.; Fedin, E. I. J. Organomet. Chem. 1996, 15, 2469.
(b) Meir, E. J. M.; Kozminski, W.; Linden, A.; Lusenberger, P.; von
Philipsborn, W. Organometallics 1996, 15, 2469. (c) Fabrello, A.;
Dinoi, C.; Perrin, L.; Kalck, P.; Maron, L.; Urrutigoity, M.; Dechy-
Cabaret, O. Magn. Reson. Chem. 2010, 48, 848.
(28) (a) Mason, J. G.; Rosenblum, M. J. Am. Chem. Soc. 1960, 82,
4206. (b) Kuwana, R.; Bublitz, D. E.; Hoh, G. L. K. J. Am. Chem. Soc.
1960, 82, 5811. (c) Langmaier, J.; Samec, Z.; Varga, V.; Horacek, M.;
Mach, K. J. Organomet. Chem. 1999, 579, 348. (d) Langmaier, J.;
Samec, Z.; Varga, V.; Horacek, M.; Choukroun, R.; Mach, K. J.
Organomet. Chem. 1999, 326, 93.
(29) This DFT methodology has been previously applied to the
study of Rh-catalysis and metal−ligand dissociation to give quantitative
agreement with experimental results: (a) Sperger, T.; Sanhueza, I. A.;
Kalvet, I.; Schoenebeck, F. Chem. Rev. 2015, 115, 9532. (b) Straker, R.;
Peng, Q.; Mekareeya, A.; Paton, R. S.; Anderson, E. A. Nat. Commun.
2016, 7, 10109. (c) Peng, Q.; Paton, R. S. Acc. Chem. Res. 2016, 49,
1042. (d) Raju, R. K.; Bengali, A. A.; Brothers, E. N. Dalton Trans.
2016, 45, 13766.
(6) Hyster, T. K.; Rovis, T. J. Am. Chem. Soc. 2010, 132, 10565.
(7) (a) Song, G.; Chen, D.; Pan, C.-L.; Crabtree, R. H.; Li, X. J. Org.
Chem. 2010, 75, 7487. (b) Rakshit, S.; Patureau, F. W.; Glorius, F. J.
Am. Chem. Soc. 2010, 132, 9585.
(8) For selected examples, see: (a) Uto, T.; Shimizu, M.; Ueura, K.;
Tsurugi, H.; Satoh, T.; Miura, M. J. Org. Chem. 2008, 73, 298.
(b) Rakshit, S.; Patureau, F. W.; Glorius, F. J. Am. Chem. Soc. 2010,
132, 9585. (c) Du, Y.; Hyster, T. K.; Rovis, T. Chem. Commun. 2011,
47, 12074. (d) Pham, M. V.; Ye, B.; Cramer, N. Angew. Chem., Int. Ed.
2012, 51, 197. (e) Neely, J. M.; Rovis, T. J. Am. Chem. Soc. 2013, 135,
66. (f) Hyster, T. K.; Ruhl, K. E.; Rovis, T. J. Am. Chem. Soc. 2013,
135, 5364. (g) Liu, G.; Shen, Y.; Zhou, Z.; Lu, X. Angew. Chem., Int. Ed.
2013, 52, 6033. (h) Davis, T. A.; Wang, C.; Rovis, T. Synlett 2015, 26,
1520. (i) Archambeau, A.; Rovis, T. Angew. Chem., Int. Ed. 2015, 54,
13337.
(9) Neely, J. M.; Rovis, T. J. Am. Chem. Soc. 2014, 136, 2735.
(10) Shibata, Y.; Tanaka, K. Angew. Chem. 2011, 50, 11109.
(11) Romanov-Michailidis, F.; Sedillo, K. S.; Neely, J. M.; Rovis, T. J.
Am. Chem. Soc. 2015, 137, 8892.
(12) (a) Hoshino, Y.; Shibata, Y.; Tanaka, K. Adv. Synth. Catal. 2014,
356, 1577. (c) Fukui, M.; Hoshino, Y.; Satoh, T.; Miura, M.; Tanaka,
K. Adv. Synth. Catal. 2014, 356, 1638.
(30) Santiago, C. B.; Milo, A.; Sigman, M. S. J. Am. Chem. Soc. 2016,
138, 13424.
(31) Winstein, S.; Holness, N. J. J. Am. Chem. Soc. 1955, 77, 5562.
(32) (a) Adams, R.; Yuan, H. C. Chem. Rev. 1933, 12, 261. (b) Bott,
G.; Field, L. D.; Sternhell, S. J. Am. Chem. Soc. 1980, 102, 5618.
(13) (a) Guimond, N.; Gouliaras, C.; Fagnou, K. J. Am. Chem. Soc.
2010, 132, 6908. (b) Guimond, N.; Gorelsky, S. I.; Fagnou, K. J. Am.
Chem. Soc. 2011, 133, 6449.
(14) Rakshit, S.; Grohmann, C.; Besset, T.; Glorius, F. J. Am. Chem.
Soc. 2011, 133, 2350.
(15) (a) Hyster, T. K.; Rovis, T. Chem. Commun. 2011, 47, 11846.
(b) Hyster, T. K.; Rovis, T. Chem. Sci. 2011, 2, 1606.
(33) Niksch, T.; Gorls, H.; Weigand, W. Eur. J. Inorg. Chem. 2010,
2010, 95.
(34) Taft, R. W. J. Am. Chem. Soc. 1953, 75, 4538.
̈
N
DOI: 10.1021/jacs.6b11670
J. Am. Chem. Soc. XXXX, XXX, XXX−XXX

Journal of the American Chemical Society
Article
(35) Charton, M. J. Org. Chem. 1976, 41, 2217.
(53) By using the normalized coefficients and eq 8 and considering
the caveats discussed above, the electronic contribution to rate is
estimated at 81%.
(36) (a) Muller, T. E.; Mingos, D. M. Transition Met. Chem. 1995, 20,
̈
533. (b) Fey, N.; Harvey, J. N.; Lloyd-Jones, G. C.; Murray, P.; Orpen,
A. G.; Osborne, R.; Purdle, M. Organometallics 2008, 27, 1372.
(c) Jover, J. S.; Fey, N.; Harvey, J. N.; Lloyd-Jones, G. C.; Orpen, A.
G.; Owen-Smith, G. J. J.; Murray, P.; Hose, D. R. J.; Osborne, R.;
Purdle, M. Organometallics 2010, 29, 6245.
(37) (a) Maitlis, P. M. Chem. Soc. Rev. 1981, 10, 1. (b) Grimmer, N.
E.; Coville, N. J. S. Afr. J. Chem. 2001, 54, 118. (c) Coville, N. J.;
Loonat, M. S.; White, D.; Carlton, L. Organometallics 1992, 11, 1082.
(d) Bauer, H.; Glockner, A.; Tagne Kuate, A. C.; Schafer, S.; Sun, Y.;
̈
̈
Freytag, M.; Tamm, M.; Walter, M. D.; Sitzmann, H. Dalton Trans.
2014, 43, 15818.
(38) (a) Corey, R. B.; Pauling, L. Rev. Sci. Instrum. 1953, 24, 621.
(b) Pauling, L. J. Am. Chem. Soc. 1931, 53, 1367.
(39) Bondi, A. J. Phys. Chem. 1964, 68, 441.
(40) Because some of the ligands contain fused ring systems (e.g.,
indenyl, tetrahydroindenyl), the ligand was partitioned into five equal
parts (72° segments for a CpX ligand), from each of which a single
contributing angle was generated that contributed to the final cone
angle.
(41) (a) Verloop, A.; Tipker, J. In Biological Activity and Chemical
Structure; Buisman, J. A., Ed.; Elsevier: New York, 1977; Vol. 63.
(b) Verloop, A. In IUPAC Pesticide Chemistry; Miyamoto, J., Ed.;
Pergamon: New York, 1983; Vol. 1. (c) Verloop, A.; Tipker, J. In
QSAR in Drug Dosing and Toxicology; Hadzi, B., Jerman-Blazic, B.,
Eds.; Elsevier: New York, 1987; Vol. 97.
(42) Harper, K. C.; Bess, E. N.; Sigman, M. S. Nat. Chem. 2012, 4,
366.
(43) Verloop, A. In Drug Design; Ariens, E. J., Ed.; Academic Press:
New York, 1976; Vol. 3.
(44) Vectors B1 and B5 describe an ellipse in the plane of the CpX
ring.
(45) (a) Presset, M.; Oehlrich, D.; Rombouts, F.; Molander, G. A.
Org. Lett. 2013, 15, 1528. (b) Webb, N. J.; Marsden, S. P.; Raw, S. A.
Org. Lett. 2014, 16, 4718. For asymmetric reactions, see: (c) Hyster,
T. K.; Knorr, L.; Ward, T. R.; Rovis, T. Science 2012, 338, 500. (d) Ye,
B.; Cramer, N. Science 2012, 338, 504.
(46) (a) Hyster, T. K.; Rovis, T. Synlett 2013, 24, 1842. (b) Semakul,
N.; Jackson, K. E.; Paton, R. S.; Rovis, T. Chem. Sci. 2017,
DOI: 10.1039/C6SC02587K.
(47) Rh12 is a superior catalyst to the previously reported Rh22 (ref
46b).
(48) Hammett, L. P. Chem. Rev. 1935, 17, 125.
(49) A Q2 value that is close to the R2 value indicates a good
predictive capability of a model.
(50) By using eq 8 and with the same caveats discussed above, the
electronic contribution to diastereoselectivity is estimated at 91%.
(51) Regression models built to describe the regioselectivity of 1-
decene insertion display no outliers with all three training sets (T1, T2,
and T3) employed; see the Supporting Information for more details.
(52) Peng, Q.; Duarte, F.; Paton, R. S. Chem. Soc. Rev. 2016, 45,
6093.
O
DOI: 10.1021/jacs.6b11670
J. Am. Chem. Soc. XXXX, XXX, XXX−XXX