The final challenge with creating these workflows is to incorporate them into existing materials discovery frameworks. Currently, the tools used for materials discovery such as AFLOW , atomate , and Aiida . Without native integration, multiple, potentially incompatible solutions must be created leading to a less transparent ecosystem. An additional benefit of fully integrating these methods is an improved selection procedure. The use of cost-aware and efficient acquisition functions is becoming increasingly popular, and including the AI model training and selection steps inside the workflow libraries themselves will improve the estimated costs for these acquisition functions and multi-fidelity approaches. An expanded use of explainable AI methods presents a clear path to achieve the necessary combination of methods presented above. By learning the conditions for screening out materials from the AI models themselves, explainable AI attempts to expands the predictive power of machine learning models, and give insights into the relationship between the input physico-chemical materials features and target properties. These methods can relate to either the regression method used, e.g. linear or symbolic regression, or post-processing techniques that uncover the relationships. By better understanding the connections between the input features and a target property, one can then replace the physics-derived heuristics with ones from the model itself.