where the number of energy intervals was equivalently set to 2,001. Thus, energy values were interpolated using "interp1d" function of "scipy" library, so that all materials are represented by DOS of the same energy range (-20 eV ~ 10 eV) and resolution (0.02 eV). When no state is available in that energy range, it was set to be 0. For materials with only one spin state, the other spin state was set to be identical with the opposite sign. In this work, the model performance showed a relatively high dependence on initial learning rate (init_lr) and batch size (bs). Thus, a hyperparameter optimization was performed with these two hyperparameters using both manual grid search and Bayesian optimization implemented in Ax through Ray package . The hyperparameter space for init_lr and bs is set to be an integer from 16 to 65, and a float from 0.0005 to 0.002, respectively. The best performance was achieved when init_lr = 0.00175 and bs = 32. We used Adam optimizer with the optimized initial learning rate of 0.00175 and learning rate schedule with plateau detection for learning rate reduction. The model was trained for 300 epochs with an early stopping algorithm on an NVIDIA GPU with memory and CUDA 11.0. The LogCosh function is selected as the loss function due to the robustness of possible outliers in high throughput databases and the precision that continuous differentiation is possible even in low errors. Mean absolute errors (MAE) and root mean squared errors (RMSE) are chosen to evaluate model performances.