In this work, we propose a deep learning model with a chemically inspired architecture optimized to featurize IS-DOS for accurate formation energy prediction. The model is based on convolutional neural network (CNN), which is suitable for high-level feature learning from multi-channel signal data . Since CNN takes inputs comprised of separate channels (orbitals) and featurizes the channels while preserving spatial information (spins), it is advantageous to featurize DOS consisting of various projected states. The featurized output (hj n ) is updated as a sum of the product of convoluted input (hk n-1 ) and weight matrices (wkj n ) (Eqn (1)). ℎ