Test dataset clusters B7, B8, B10, B12, B13, B16, B18, B19, B23, B26, B27, B28, B30, B31, B32, B33, B34,B35, B36, B38, B39, B40 B4, B6, B9, B15, B20, B21, B22, B29 B5, B11, B14, B17, B24, B25, B37 Accordingly, the training dataset consists of 22 boron clusters which generate 67.320 data points. Validation dataset consists of 8 boron clusters which corresponds to 24.480 data points. The test dataset consists of 7 boron clusters which corresponds to 21.420 data points. Distribution of the datasets is presented in Table . The model is trained using Matlab, and the data is trained for 50 epochs. The initial learning rate is kept at 0.002 and the learning rate is lowered after 20 epochs, with a learn-rate drop factor of 0.1. Early stopping is utilized to avoid an overfitting. In early stopping, the variation between the evolving trend of the loss on training and validation is seen, then the training is stopped when the variation increases between training and validation increases by a large margin. The time required to train the model amounts to 1100 minutes (~18.3 hours). The architecture of the CNN model constructed is given in the electronic Supporting Information file (ESI).