Trapped at Tier 1: Why machine learning-guided electrocatalyst discovery has not closed the lab-to-industry gap

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Machine learning has transformed electrocatalyst discovery, enabling screening across millions of candidate compositions. Yet publications on HER/OER have grown by over 63% since 2014, while no comparable benchmarking tracks whether this effort has translated into measurable gains in commercial alkaline water electrolyzer (AWE) performance. This Perspective argues the disconnect is structural: every ML-guided workflow surveyed here validates at Tier 1 (rotating disk electrode, ≤10 mA cm⁻², 25°C), while industrial AWE operates at 200–1200 mA cm⁻², 80–90°C, and 30 wt% KOH — conditions characteristic of Tier 3 (AWE/PEMWE stack) — separated from Tier 1 by four phenomena absent from all ML training datasets: bubble-induced mechanical stress, dissolution under dynamic load cycling, binder–catalyst interface degradation, and high-current mass transport limitations. Tier 2 (flow cell/beaker cell at ≥200 mA cm⁻², 60–80°C, industrial electrolyte) sits between these extremes and is equally absent from published ML training pipelines. No closed-loop workflow surveyed here has completed retraining using Tier 2 or Tier 3 stability data. We define this three-tier benchmarking framework — Tier 1 (RDE), Tier 2 (flow cell), Tier 3 (stack) — identify the structural data gaps, and propose a tier-informed ML architecture with concrete recommendations for data infrastructure.

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