As the user complexity and model complexity of ML projects grow, it is important that access to, and understanding of, ML tools do not become a barrier for their use. Cloud platforms such as the LMDS provide easier access to these tools, however it is on a per-practitioner basis to portray the understanding of best use and interpretation of such tools. For example many ML models (such as that used in the thermal conductivity prediction model) take composition as input, but have no mechanisms to check the chemical viability of such compositions. Without adequately communicating this limitation with collaborators, the trust in such a model may be hindered, and the interpretation of its results will be incomplete. While explainability and uncertainty estimates in ML are active research areas , a good understanding of the limits and correct usage of ML models by those who use them is also important. Drawing up interesting counter examples when presented with predictive models to demonstrate their limits is a valid method of testing the capabilities of models. This may not be a particularly useful test in many cases, as we know that statistical models will under perform on chemical domains they have never been exposed to, and this may not be how the model should be used in practice. For example, the thermal conductivity model (Section 2.5) is trained only on thermoelectric materials, while it may be used to predict thermal conductivity for other materials, it may under-perform outside the thermoelectric domain.