New research explores when AI models can safely replace experiments

A new paper explores the safety and cost of using machine learning models as surrogates for experiments in design tasks across chemistry, materials science, and machine learning. The research establishes conditions under which this practice is reliable, highlighting that predictive accuracy alone is insufficient for trust. Instead, safety depends on an architectural rule: certified conclusions must be based on true evaluations, not model predictions. The study derives criteria for models acting as oracles and demonstrates that audited surrogates can significantly reduce the cost of certified evaluations. AI

IMPACT Provides a framework for understanding the reliability and cost-effectiveness of using AI models in scientific design processes.

RANK_REASON The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• Shuangixu(Max) Ma

• paper
• safety
• other

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