# Predicting the Stability of Base‐mediated C─H Carboxylation Adducts Using Data Science Tools

**Authors:** Maike Eckhoff, Shubham Deolka, Aleria Garcia‐Roca, Lilly Meynberg, Liudmila Seidel, Matthew S. Sigman, Jonny Proppe

PMC · DOI: 10.1002/anie.202504934 · Angewandte Chemie (International Ed. in English) · 2025-11-19

## TL;DR

This paper introduces a computational method combining quantum chemistry and machine learning to predict the stability of CO2 adducts in C–H carboxylation reactions.

## Contribution

A novel predictive workflow integrating quantum chemistry and statistical modeling for CO2 adduct stability.

## Key findings

- The workflow was applied to 60 nucleophiles, identifying reactions that yield stable carboxylation adducts.
- Experimental validation confirmed predictions for five carbanions, including three stable and two unstable adducts in DMSO.
- The method was extended to assess structurally distinct carbanions for broader applicability.

## Abstract

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 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 CO2 adducts in DMSO, to further assess the experimental protocol and broaden its scope to structurally distinct motifs.

A computational approach integrating quantum chemistry and machine learning enables the prediction of CO2–adduct stability. Model interpretability and experimental validation highlight its utility for designing base‐mediated C–H carboxylation reactions.

## Linked entities

- **Chemicals:** CO2 (PubChem CID 280), DMSO (PubChem CID 679)

## Full-text entities

- **Chemicals:** Base (-), carbon (MESH:D002244), DMSO (MESH:D004121), CO2 (MESH:D002245)

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12790316/full.md

## References

47 references — full list in the complete paper: https://tomesphere.com/paper/PMC12790316/full.md

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Source: https://tomesphere.com/paper/PMC12790316