Although the MSE loss is very small across realizations (Fig. ) and across training datasets (Fig. ), the mixing performance for extrapolation in terms of the number of interfaces is more sensitive for both [Fig. It is evident that the data chosen to train the system here (single black-white initial conditions and LS method descendants), as well as possibly the loss function and NN architecture, are insufficient to develop an NN that understands LS mixing algorithmically. The trained NNs can replicate the pattern in the LS method training data and interpolate that pattern to new data similar to the training data, but they are unable to demonstrate performance consistent with an algorithmic understanding sufficient to reliably extrapolate to new configurations. This raises questions about the distinctions between "functions" and "algorithms," which ML architectures best encapsulate algorithmic logic, and how such a system would be trained. Machine learning has been quite successful in identifying patterns, finding optimal conditions, and fitting various types of data . However, we attempt here to use ML to train a NN to implement an algorithm . In this case, the NN learns a pattern from the exact LS method training data that is sufficient to allow the NN to mix systems similar to those it was trained on and continue mixing even as error accumulates. However, the NNs as constructed and trained here are unable to reliably extrapolate the algorithm beyond configurations upon which they are trained. Training ML systems for robust inputs that do not fit in the same distribution as the training data is a constantly evolving field of research known as out of distribution generalization that should be considered in extending this work .