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 resolve these mechanistic branch points, primarily relying on static ground-state descriptors or bulk structural correlations rather than end-to-end topological pathway analysis. Here, we show that fr...
| Authors | Sutanay Choudhury, Anwesha Banerjee, Udishnu Sanyal et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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