NMR spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity to manually analyze NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds, and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain (HMCMC) and density functional theory (DFT) to fit predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of spectra of containing mixtures of compounds, without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C--H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with mean absolute error as low as 1%.
Deconvolution and Analysis of 1H NMR Spectra of Crude Reaction Mixtures
25 September 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.