The formation energy model is a fully connected neural network with residual layers. The model consists of 7 blocks of layers with each block consisting of a dense layer followed by a batch normalization layer, a LeakyRelu layer and a dropout layer. The seventh block does not have a dropout layer and is connected to a single neuron, which outputs the formation energy. The input to each block is the output of the previous block combined with the original input. Mean absolute error (MAE) was used as the loss metric to assess the performance of the model. Adam optimizer was used with default parameters.