White paper
June 3, 2022

An exhaustive guide to Explainable AI for Material Science & Informatics

High-performance AI models have struggled to explain their predictions. This white paper shares a practical, solution-centric application of Explainable AI for material synthesis using SHAP.

What's inside

  • Why high-performing models still fail to explain their predictions, and what it costs R&D teams
  • A closed-loop strategy that pairs AI engineers with polymer scientists
  • Explainable AI for material synthesis with SHAP, including code and fundamental guidelines
  • Lessons from building and enhancing the Polymerize Labs AI engine over time

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