In this work, a novel analysis approach using an artificial intelligence (AI) framework that automatically helps identify the drivers of a formulation from lab measurements was showcased. This AI framework builds and optimizes models in the form of physics-based equations from small amounts of measurement data. With this approach, the user can overcome the large data requirements of machine learning while building tailored models that outperform traditional analytical and statistical tools.
The effectiveness of this modeling framework in helping scientists reduce uncertainty early in the experiment process was demonstrated. This solution allows a significant reduction in the number of experiments required to achieve an optimal formulation. This was accomplished by generating new, custom models from existing data and well-known equations in electrochemistry. Then, these models were used to predict or hypothesize the performance of unseen formulations by altering their control parameters. This study showed the accuracy of these predictions by calculating its error against unseen measurement data.