Neural networks and machine learning algorithms have been used in materials science and engineering for some years now and have even yielded successes in developing new materials and novel manufacturing methods. However, the majority of this research is based upon learning data sets that try to link numerical materials property data to the manufacturing process variables. Such approaches have limited potential because the microscopic structure of the materials that actually determines the properties and its evolution during processing is not taken into account explicitly. As a result, the trained machine learning models are able to interpolate well the possibilities that fall with the domain of the training data, but often fail to make viable predictions outside of it. Thus, potentially superior novel processing methods and materials with improved properties can remain undiscovered.