Maike Eckhoff

Base‐mediated C–H carboxylation is a versatile pathway for utilizing carbon dioxide (CO ₂ ) as a C1 building block in organic synthesis. However, CO ₂ constitutes a notorious thermodynamic sink, which restricts this approach to activated or intrinsically reactive nucleophiles. To qualitatively assess the stability of CO ₂ adducts, we present a computational approach that integrates quantum chemistry with statistical modeling to build a predictive workflow. The target property is the CO ₂ affinity, specifically the negative Gibbs free reaction energy. This predictive workflow has been applied to 60 novel carbon‐centered nucleophiles, suggesting reactions that yield stable carboxylation adducts. The results have been validated through experimental methods for five carbanions, which include three stable and two unstable adducts in DMSO according to our predictions. In addition, we examined two further carbanions that were suggested to form stable CO ₂ adducts in DMSO, to further assess the experimental protocol and broaden its scope to structurally distinct motifs.

Base‐mediated C–H carboxylation is a versatile pathway for utilizing carbon dioxide (CO ₂ ) as a C1 building block in organic synthesis. However, CO ₂ constitutes a notorious thermodynamic sink, which restricts this approach to activated or intrinsically reactive nucleophiles. To qualitatively assess the stability of CO ₂ adducts, we present a computational approach that integrates quantum chemistry with statistical modeling to build a predictive workflow. The target property is the CO ₂ affinity, specifically the negative Gibbs free reaction energy. This predictive workflow has been applied to 60 novel carbon‐centered nucleophiles, suggesting reactions that yield stable carboxylation adducts. The results have been validated through experimental methods for five carbanions, which include three stable and two unstable adducts in DMSO according to our predictions. In addition, we examined two further carbanions that were suggested to form stable CO ₂ adducts in DMSO, to further assess the experimental protocol and broaden its scope to structurally distinct motifs.

We present a data-driven approach that integrates supervised learning, quantum chemistry, and uncertainty quantification to determine CO₂ reactivity, enabling advances in carbon capture and the design of value-added chemicals.

Base-mediated C–H carboxylation is a versatile pathway for utilizing carbon dioxide (CO2) as a C1 building block in organic synthesis. However, CO2 constitutes a notorious thermodynamic sink, which restricts this approach to activated or intrinsically reactive nucleophiles. To qualitatively assess the stability of CO2 adducts, we present a computational approach that integrates quantum chemistry with statistical modeling to build a predictive workflow. The target property is the CO2 affinity, specifically the negative Gibbs free reaction energy. This predictive workflow has been applied to 60 novel carbon-centered nucleophiles, suggesting reactions that yield stable carboxylation adducts. The results have been validated through experimental methods for five predicted nucleophiles, which include three stable and two unstable adducts.

Sulfur-derived functional groups represent prevalent motifs in highly sought-after small molecules, such as active pharmaceutical ingredients (APIs). Thioethers are one such example, being commonly encountered in APIs, prodrugs, and as valuable synthetic "linchpins" to access an array of sulfur-derived functional groups. While nucleophilic aromatic substitution (S_NAr) has traditionally been used to synthesize aryl thioethers, modern approaches leverage transition metals to catalyze thermal or photochemical cross-coupling. While studying photochemical thioetherification reactions, we uncovered a remarkably mild condition that does not require light, transition metals, or exogenous bases. An array of thiols and halogenated heterocycles were coupled to produce >70 diverse products. Reaction progress kinetic analysis (RPKA) and computational studies support a unique mechanism termed proton transfer dual ionization (PTDI) S_NAr. Finally, a predictive statistical model was constructed aided by high-throughput experimentation (HTE) to understand when the PTDI processes are successful, resulting in the completion of ten target-oriented syntheses. This transformation complements modern approaches to thioether synthesis and motivates additional research evaluating PTDI as a general activation mode between reaction partners.

Sulfur derived functional groups represent prevalent motifs in highly sought after small molecules such as active phar-maceutical ingredients (APIs). Thioethers are one such example, being commonly encountered in APIs, prodrugs, and as valuable synthetic ‘linchpins’ to access an array of sulfur derived functional groups. While nucleophilic aromatic substi-tution (SNAr) has traditionally been used to synthesize aryl thioethers, modern approaches leverage transition metals to catalyze thermal or photochemical cross-coupling. While studying photochemical thioetherification reactions, we uncov-ered a remarkably mild condition that does not require light, transition metals or exogenous base. An array of thiols and halogenated heterocycles were coupled to produce >70 diverse products. Reaction progress kinetic analysis (RPKA) and computational studies support a unique mechanism, termed proton transfer dual ionization (PTDI) SNAr. Finally, a predic-tive statistical model was constructed aided by high throughput experimentation (HTE) to understand when the PTDI processes are successful, resulting in the completion of ten target-oriented syntheses. This transformation complements modern approaches to thioether synthesis and motivates additional research evaluating PTDI as a general activation mode between reaction partners.

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. 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 = –14.6(5) with 95 % confidence.

Selective and feasible reactions are among the top targets in synthesis planning. Mayr's approach to quantifying chemical reactivity has greatly facilitated the planning process, but reactivity parameters for new compounds require time-consuming experiments. In the past decade, data-driven modeling has been gaining momentum in the field, as it shows promise in terms of efficient reactivity prediction. However, state-of-the-art models use quantum chemical data as input, which prevent access to real-time planning in organic synthesis. Here, we present a novel data-driven workflow for predicting reactivity parameters of molecules that takes only structural information as input, enabling de facto real-time reactivity predictions. We use the well-understood chemical space of benzhydrylium ions as an example to demonstrate the functionality of our approach and the performance of the resulting quantitative structure-reactivity relationships (QSRRs). Our results suggest that it is straightforward to build low-cost QSRR models that are accurate, interpretable, and transferable to unexplored systems within a given scope of application. Moreover, our QSRR approach suggests that Hammett sigma parameters are only approximately additive.