An exhaustive guide to Explainable AI for Material Science & Informatics
High-performance Machine Learning / AI algorithms have been inadequate in precisely explaining their so-called accurate predictions.
In the majority of cases, there are fundamental issues ( overfitting, high complexity, generalization errors ) with the models and/or the process that haunt engineers later, after they’ve invested a significant amount of time and resources, eventually finding out the discrepancy.
Our team of experts ( Polymer and AI Scientists ) solved this problem by using a comprehensive closed-loop strategy.
The AI engineers made sure they open the black-box models (with explainability and interpretability), to quantify and highlight relationships between the input formulations and output properties in the data.
Consequently, Polymer Scientists were responsible for analyzing and validating given relationships with academic research and development frameworks of the industry before approving the model for usage.
Power your materials research with Polymerize today
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