Three hidden layers of 30, 15 and 5 nodes respectively using the 'exponential linear unit' activation function were added, and right after each hidden layer, three dropout layers, with dropout rate of 0.3, 0.2 and 0.1 were added to randomly reduce the size of hidden nodes to avoid overfitting. This was eventually followed by an output layer of one node, also with 'elu' activation function, which approximates the molecular expected cost. For specification, MLP used learning rate of 0.002 to slowly learn from data, 'mean square error' as the loss function,