MHz. To further assess the performance of the models, we also compared our models with a benchmark model using the SOAP features . The SOAP model was also trained with a random forest algorithm based on the same set of data as our model. The only difference is the features used for training. Instead of using our structural and chemical features, we use SOAP features generated by an open-source package Dscribe , the total number of SOAP features generated for our data set is 4163280. Figure shows a performance comparison between models based on our proposed features (structure, structural + elemental) and the model based on SOAP. As shown in Figure , both of our proposed features perform significantly better than SOAP no matter the size of the sample. Structural + elemental features performs better than structural features when the sample size gets larger. Most importantly, the SOAP features result in strong outliers. This shows that a simple set of features that are customized for a specific problem such as NMR parameter prediction can outperform universal features because this method excludes a lot of unnecessary information that could significantly decrease the performance of the model in terms of both efficiency and accuracy.