The encoder encodes the information present in our representation in a lower-dimensional latent space. This latent space is structured into different regions for the stable and unstable crystals. We use a 1D convolutional neural network (CNN) to reduce the dimensionality of our input and map it to a latent space. This approach is followed to capture the spatial dependence of our crystal representation. Similar approaches have been used before for 3D image classification as well as in materials space . Two layers of 1D CNN of kernel size (5,3), strides (1,2), and padding (2,1) were used. The number of channels are 16 and 32, respectively. After the convolutional layer, we flatten the array to output 1024 neurons. This is further reduced to size 100 to optimize the performance, which is the dimensionality of our latent space, before being upscaled back to size 2208. We use leakyRELU activation layer (parameter = 0.2). Batch Normalization is used between the layers.