We independently trained the model 4 times in the last iteration of active learning and all models consistently reached early-stop at rough epoch 250 (Figure ). The nearest neighbor model with 44 parameters , including 2 initiations, 10 stacks and 32 dangles, was obtained by the multiple linear regression on the same training data. The results show that our model has significantly better prediction than the nearest neighbor model on the test set(Figure ). Specifically, the accuracy of our model's predicted and observed values within a factor of 2 is 72.8%, while the nearest neighbor model is 50.0%. the accuracy of our model's predicted and observed values within a factor of 3 is 91.7%, while the nearest neighbor model is 70.8%. To quantify the predictive performance, we compared the RMSE between our model and the nearest neighbor model on the test set, and the improvement is 20.3% (0.349 versus 0.438). This significant improvement is due to the accuracy of quantum chemistry calculations coupled with the nonlinear fitting ability of deep learning models, which makes our model perform better in prediction compared to the traditional model.