Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses

Catalysts are essential for sustainable chemical manufacturing, yet discovering novel architectures remains a bottleneck dominated by trial-and-error experimentation and computationally intensive screening. In complex reactions such as electrochemical carbon dioxide reduction, product selectivity is governed by dynamic interfacial, electrolyte, and potential factors as well as kinetic pathway competition. Conventional descriptor-based machine learning and computational potentials struggle to res

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ethics.ai (9 July 2026), “Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses,” evidence record 106, https://ethics.ai/record/106 (originally published by arXiv).

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