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Bridging Machine Learning and Water Electrolysis: Concepts, Methods, and Perspectives
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DOI：10.1016/j.mtener.2026.102232.png)
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Abstract
En 中文
• Provides original schematic illustrations of the principles of key ML algorithms • Introduces ML concepts and workflows in a non-coding manner • Compares Python-based, low-code and no-code ML tools • Discusses catalyst screening, spectroscopic analysis, and device monitoring • Bridges conceptual understanding and practical ML workflows for chemists
Keywords:
Machine Learning
Water Electrolysis
Schematic Illustrations
No-Code Tools
Cheminformatics
Journal
IF:
8.6
Papers:
2.3K
Citations:
1.2W