A Quantitative Model for Alkane Nucleophilicity Based on C−H Bond Structural/Topological Descriptors

Abstract

A first quantitative model for calculating the nucleophilicity of alkanes is described. A statistical treatment was applied to the analysis of the reactivity of 29 different alkane C−H bonds towards in situ generated metal carbene electrophiles. The correlation of the recently reported experimental reactivity with two different sets of descriptors comprising a total of 86 parameters was studied, resulting in the quantitative descriptor‐based alkane nucleophilicity (QDEAN) model. This model consists of an equation with only six structural/topological descriptors, and reproduces the relative reactivity of the alkane C−H bonds. This reactivity can be calculated from parameters emerging from the schematic drawing of the alkane and a simple set of sums.

Regiospecific and Enantioselective Arylvinylcarbene Insertion of a C–H Bond of Aniline Derivatives Enabled by a Rh(I)-Diene Catalyst

“…A systematic DFT (SMD B3LYP-D3/6-31G*+SDD­(Rh) method mainly) study was also carried out by using the chiral Rh­(I)-diene catalyst, [Rh­( L1 )­Cl] 2 , as well as substrates 1a and 1-phenylpyrrolidine. ,, The relative stability of several possible isomers of the active Rh­(I)-vinylcarbenoid intermediate ( II in Scheme ) was first examined (Scheme ). As reported previously, , our DFT calculations generally show that the s-cis and s-trans conformations of the Rh­(I)-vinylcarbenoid intermediate have comparable stability.…”

tmQM Dataset—Quantum Geometries and Properties of 86k Transition Metal Complexes

“… 9 − 11 Neural networks 12 − 16 and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science 17 − 21 and drug discovery. 22 − 26 ML and data-driven approaches are also making rapid progress in catalytic, 27 − 41 organic, 42 − 47 inorganic, 48 , 49 and theoretical 50 − 56 chemistry.…”

“…These models can robustly handle data sets that can be both very large and complex and, once compiled, can compute accurate predictions in a simple laptop within a fraction of a second. The fast execution of ML predictions enables the exploration of the vast chemical compound space (CCS) − with different approaches, including multi-objective optimization and inverse design. − Neural networks − and other ML models have been used successfully in a wide range of applications, with numerous examples in materials science − and drug discovery. − ML and data-driven approaches are also making rapid progress in catalytic, − organic, − inorganic, , and theoretical − chemistry.…”

Iron‐Catalyzed Intermolecular Functionalization of Non‐Activated Aliphatic C−H Bonds via Carbene Transfer

“…We are aware that recent work has provided a multiparametric analysis of the factors that govern CÀ H site selectivity in carbene transfer to alkanes by copper, rhodium and silver catalysts, from where metal dependent trends have been derived. [25] Future work will be directed to place iron based catalysts in this frame. Furthermore, we envision that further elaboration of this catalyst will provide a novel generation with improved activity and selectivity and will open the path towards enantioselective transformations.…”