For comparability, training and hyperparameter optimization follows the same 4:1 training-test set separation and five-fold cross validation scheme as in the scalar learning approach. As only one model is trained this time though, the global Box-Behnken minimization instead uses the average MAE of CQ , η, and the quaternion dot product, q as performance metrics. Again we obtain a comparable learning performance for all three nuclei, with the results for 47 Ti and 17 O provided in the SI, Fig. summarizes the achieved predictions for all Li in the test set, where for a direct comparison with the scalar learning results, the polar and azimuthal angles θ and ϕ were extracted from the learned quaternions. The correlation for CQ and η is now excellent, with Pearson correlations of r = 1.00 and r = 0.98, respectively, far surpassing the already good correlation achieved in the scalar learning case. The MAE for CQ and η is 0.06 and 0.04, respectively. Even more impressive is the improvement in the case of the angles θ and ϕ. They now also show a good agreement at only slightly lower Pearson correlations of r = 0.82 and r = 0.78, respectively. The corresponding MAE is 0.15 radians or an acceptable 8.6 • for θ, and 0.22 radians or 13 • for ϕ.