Unveiling CO2 reactivity with data-driven methods

Abstract

Carbon dioxide is a versatile C1 building block in organic synthesis. Understanding its reactivity is crucial for predicting reaction outcomes and identifying suitable substrates for the creation of value-added chemicals and drugs. A recent study [Li et al., J. Am. Chem. Soc., 2020, 142, 8383] estimated the reactivity of CO₂ in the form of Mayr's electrophilicity parameter E on the basis of a single carboxylation reaction. The disagreement between experiment (E = −16.3) and computation (E = −11.4) corresponds to a deviation of up to ten orders of magnitude in bimolecular rate constants of carboxylation reactions according to the Mayr–Patz equation, log k = s_N(E + N). Here, we introduce a data-driven approach incorporating supervised learning, quantum chemistry, and uncertainty quantification to resolve this discrepancy. The dataset used for reducing the uncertainty in E(CO₂) represents 15 carboxylation reactions in DMSO. However, experimental data is only available for one of these reactions. To ensure reliable predictions, we selected a training set composed of this and 19 additional reactions comprising heteroallenes other than CO₂ for which experimental data is available. With the new data-driven protocol, we can narrow down the electrophilicity of carbon dioxide to E(CO₂) = −14.6(5) with 95% confidence, and suggest an electrophile-specific sensitivity parameter s_E(CO₂) = 0.81(6), resulting in an extended reactivity equation, log k = s_Es_N(E + N) [Mayr, Tetrahedron, 2015, 71, 5095].