2022
DOI: 10.1016/j.egyr.2022.09.135
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Machine learning-based simulation for proton exchange membrane electrolyzer cell

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Cited by 17 publications
(1 citation statement)
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“…As such, involving artificial intelligence and data science in PEMWE development for comprehensive investigation is desirable for accurately optimizing electrolyzer design and predicting the best structure of potential catalysts based on algorithms. For example, machine learning can guide the design of a PEMWE electrolyzer by fast and easy simulation of the hydrogen production rate and cell current density of the electrolyzer by correlating with various design parameters to obtain the optimal result to guide the subsequent experiments [175]. Hence, assistance by machine learning offers the best approach to achieving high performance at the shortest possible time.…”
Section: Discussionmentioning
confidence: 99%
“…As such, involving artificial intelligence and data science in PEMWE development for comprehensive investigation is desirable for accurately optimizing electrolyzer design and predicting the best structure of potential catalysts based on algorithms. For example, machine learning can guide the design of a PEMWE electrolyzer by fast and easy simulation of the hydrogen production rate and cell current density of the electrolyzer by correlating with various design parameters to obtain the optimal result to guide the subsequent experiments [175]. Hence, assistance by machine learning offers the best approach to achieving high performance at the shortest possible time.…”
Section: Discussionmentioning
confidence: 99%