For the supervised learning approach, NNs are trained by adjusting the weights, 𝑤 𝑖,𝑗,𝑘 , and biases, 𝑐 𝑗,𝑘 , [Eq. ] to reduce error between the NN output and the training data, i.e., the cut locations specified by the LS method. This error, called the loss, is computed by a specified loss function. Given a set of configurations to mix, the network produces cut location predictions, { χ𝑖 }, that are compared with the LS method cut locations, {𝜒 𝑖 }, via a mean-squared-error (MSE) loss function,