Trusting the Experiment
How AI adoption, model uncertainty, and transparent workflows can improve materials and chemicals product development
Many product development teams already believe AI can help. The harder problem is getting more scientists to use AI consistently, understand its recommendations, and trust those recommendations enough to act. This is especially important when AI suggests an experiment that looks unintuitive, carries uncertainty, or does not resemble the safest variation on what worked before.
The full value of AI is not realized when a model simply predicts a better candidate. It is realized when scientists can use AI to make better experimental decisions, including decisions that help the model learn.
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