Bayesian probabilistic assignment of chemical shifts in organic solids

A pre-requisite for NMR studies of organic materials is assigning each experimental chemical shift to a set of geometrically equivalent nuclei. Obtaining the assignment experimentally can be challenging and typically requires time-consuming multi-dimensional correlation experiments. An alternative solution for determining the assignment involves statistical analysis of experimental chemical shift databases, but no such database exists for molecular solids. Here, by combining the Cambridge structural database with a machine learning model of chemical shifts, we construct a statistical basis for probabilistic chemical shift assignment of organic crystals by calculating shifts for over 200,000 compounds, enabling the probabilistic assignment of organic crystals directly from their two-dimensional chemical structure. The approach is demonstrated with the 13C and 1H assignment of eleven molecular solids with experimental shifts, and benchmarked on 100 crystals using predicted shifts. The correct assignment was found among the two most probable assignments in over 80% of cases.

有机材料核磁共振（NMR）研究的前提条件是将每个实验测得的化学位移分配给一组几何等价的原子核。通过实验获取这种分配结果具有挑战性，通常需要耗时的多维相关实验。确定化学位移分配的另一种方案是对实验化学位移数据库进行统计分析，但目前尚无针对分子固体的此类数据库。本文通过将剑桥结构数据库（Cambridge Structural Database）与化学位移机器学习模型相结合，通过计算超过20万个化合物的化学位移，构建了有机晶体概率化学位移分配的统计基础，从而能够直接根据有机晶体的二维化学结构进行概率分配。该方法通过对11种具有实验位移数据的分子固体进行¹³C和¹H化学位移分配验证，并使用预测位移对100种晶体进行基准测试。在超过80%的案例中，正确分配结果均位于两个最可能的分配结果之中。