Nuclear magnetic resonance (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 of manually analyzing the 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 and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the 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 a mean absolute error as low as 1%.

中文翻译：

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粗反应混合物的 1H NMR 谱的解卷积和分析

核磁共振 (NMR) 波谱是合成有机化学中的一项重要分析技术，但由于需要手动分析新化学实体的 NMR 波谱，因此其与高通量实验工作流程的集成受到限制。当前对核磁共振谱进行自动化分析的努力依赖于与已知化合物报告的谱数据库的比较，因此与新化学空间的探索不相容。通过将反应混合物的核磁共振谱重新构建为联合概率分布，我们使用哈密顿蒙特卡罗马尔可夫链和密度泛函理论将预测的核磁共振谱与粗反应混合物的核磁共振谱进行拟合。这种方法能够对化合物混合物的光谱进行解卷积和分析，而不依赖于报告的光谱。我们的方法分析粗反应混合物的实用性通过产生异构体混合物的反应的实验光谱得到了证明，例如维蒂希烯化和 C-H 官能化反应。正确识别反应混合物中的化合物及其相对浓度，平均绝对误差低至 1%。