Here we train multiple neural networks (NNs) with identical structure but different training data to replicate the LS method for configurations that appear normally in the LS method when starting with fully segregated, single black-white initial conditions. In the process of mixing, the black and white segments composing the domain become increasingly uniform and increasingly fine, demanding increased accuracy and the ability to choose between (nearly) equivalent options. For a human, reducing the largest unmixed segments by applying the LS method is a trivial process, but this is not the case for a NN.