High-performance AI algorithms in material informatics have been inadequate in precisely explaining predictions. In most cases, there are fundamental issues with the models and processes that create discrepancies in the outcomes sought by R&D researchers. Polymerize's team of experts (Polymer andAI Scientists) created a solution using a comprehensive closed-loop strategy.
This white paper covers a practical, solution-centric application of Explainable AI for Material Synthesis using a SHAP algorithm. It also covers insights on how this engine can be enhanced over time.