Relevance: Finally, as the performance of machine learning models improves, it is worth keeping in mind that these models are intended to be a means to an end rather than the end in themselves. There are many examples where ML was helpful to an outcome even if it was not perfectly optimized . Conversely, there are also examples where ML models obtain good scores on one metric that does not always translate to seemingly related metrics. Examples include formation enthalpy scores not translating to phase stability or MAE scores translating to materials discovery acceleration factors . Furthermore, materials or design suggestions are not valuable unless they are eventually made and tested.