This approach highlights the application of ExpNN-ff as high-throughput method to detect assignment errors, however, the incorporation of sophisticated metrics such as Goodman's DP4 18 would be necessary for a more rigorous evaluation of possible structural assignments, and is the subject of further work. Application as atomic descriptors in selectivity prediction: NMR Chemical shift is influenced by the electron density around a nucleus of interest. It is therefore an attractive choice of physically-motivated and interpretable atomic descriptor for use in predictive machine learning models. By foregoing expensive quantum chemical computations, chemical shifts accurately predicted by ExpNN-ff provide easier and faster access to descriptors for use in regression tasks such as reactivity and selectivity prediction. We have investigated this approach in predicting the regioselectivity of electrophilic aromatic substitution (EAS) reactions. Previously, the combination of DFT-computed atomic Fukui coefficients, atomic partial charges, bond orders, and partitioned solvent-accessible surface areas with semi-empirical regioSQM predictions was used to develop a random forest (RF) model with 93% accuracy in predicting the site of substitution using 80/20 train/test splits for 376 molecules. Below (Fig. ) we demonstrate comparable accuracy with fewer atomic descriptors, using just (i) the C chemical shift, (ii) the attached proton 1 H chemical shift, and