Hi, I developed the model. We are not bypassing the regular testing process, and are not using LLMs, but Gaussian processes with vetted test data. The predictions are used as recommendations for onsite testing, to accelerate finding mixtures with optimal strength-speed-sustainability trade-offs.

That’s helpful. So instead of a much larger test matrix you are using a model to reduce that to the most likely candidates, right?

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