Take one problem: predict mixture viscosity across composition and temperature.
Black-box ML
Strengths: flexible fit when data is dense and in-domain.
Failure mode: invents non-physical behaviour under sparse data or extrapolation; hard to defend in a design review.
Pure first-principles
Strengths: physically plausible, data-efficient for ideal systems.
Failure mode: misses synergies and impurities that dominate real formulations.
Physics-informed hybrid
prediction = baseline + ML residual ± uncertainty
You keep physical plausibility, learn what the baseline misses, and show confidence. That is the Lavoisier default — detailed on Technology and implemented in Studio Pro.
