Reproducibility, Benchmarks, and Mechanistic Diversity: Building Trust in Computational Electrocatalysis for the AI Era

Computational electrocatalysis has contributed significantly to the design of electrocatalysts for energy conversion and storage. Yet three avoidable shortcomings constrain the reliability and reusability of computational results: incomplete reporting, oversimplification of reaction pathways and/or active sites considered, and inconsistent protocols for constructing free-energy changes (ΔG). Researchers may therefore be forced to repeat calculations simply to recover structures, verify reference conventions, or identify the source of conflicting results. This Perspective argues that reproducibility must encompass the full computational record: electronic-structure settings, surface models, atomic configurations, thermochemical and electrochemical corrections, and the explored reaction network. We examine the roles of surface structure, coverage, stability, mechanistic breadth, and descriptor choice under operating bias, and propose modular benchmarks spanning numerical verification, surface chemistry, electrified interfaces, and mechanistic validation. We also outline how journals and repositories can strengthen methodological transparency and Findable, Accessible, Interoperable, and Reusable (FAIR) data deposition. Our aim is to stimulate discussion around a persistent reproducibility deficit that leads to inefficient use of computational resources, obstructs cumulative science, and leaves valuable data unavailable for benchmarking, data mining, and trustworthy artificial-intelligence-assisted discovery. More complete and standardized computational records would make results both more credible today and substantially more reusable tomorrow.