NCHU Team Advances Hydrogen Energy Research with Artificial Intelligence — Findings Featured in Journal of Materials Chemistry A
2025-10-27
興新聞張貼者
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Source: College of Engineering, National Chung Hsing University
As the world strives to reduce carbon emissions and develop clean energy, hydrogen has emerged as one of the most promising green energy sources. Among various hydrogen production methods, electrocatalytic water splitting stands out; however, its key reactions face high energy barriers that have long limited efficiency improvement.
To overcome this challenge, a research team from National Chung Hsing University (NCHU) has combined artificial intelligence (AI) machine learning algorithms with electrocatalyst synthesis techniques, successfully developing a new process optimization model that significantly enhances hydrogen generation efficiency through water splitting. Their breakthrough has been published in the prestigious international journal Journal of Materials Chemistry A and was featured as a cover article in the issue.
This study is part of the National Science and Technology Council (NSTC) Frontier AI Research Program, under the “Thematic Research Group Project.” The interdisciplinary team includes Professor Ming-De Yang, Dean of the College of Engineering; Professor Chih-Ming Chen, Vice Dean; Professor Hung-Chung Lee; and Professor Hou-Chien Chang, with contributions from postdoctoral researcher Chandrasekaran Pitchai and master’s student Ting-Yu Lo.
The team developed a new material, vanadium-doped nickel-cobalt layered double hydroxide (NiCoV LDHs), and used an AI model to analyze parameters such as material composition, electrolyte concentration, and synthesis temperature. The model accurately predicted the optimal synthesis and reaction conditions. Under these AI-optimized conditions, the hydrogen production efficiency improved by 21.4% compared to the best prior experimental results, with only a 6.1% deviation between model prediction and experimental verification — demonstrating remarkable precision. Further analyses showed that the new material offers superior electrical conductivity and long-term stability during reactions.
Notably, the AI system required fewer than 50 experimental data sets to predict nearly one million possible outcomes, reducing time and material costs by over 99% compared to traditional trial-and-error approaches.
Professor Chih-Ming Chen emphasized that this technology not only accelerates new material development but also helps future scientists minimize repetitive experimentation in energy material design. “This research demonstrates that AI can do far more than write essays, play chess, or chat — it can drive breakthroughs in energy science and help humanity transition faster toward a sustainable green energy future,” Chen said.
As the world strives to reduce carbon emissions and develop clean energy, hydrogen has emerged as one of the most promising green energy sources. Among various hydrogen production methods, electrocatalytic water splitting stands out; however, its key reactions face high energy barriers that have long limited efficiency improvement.
To overcome this challenge, a research team from National Chung Hsing University (NCHU) has combined artificial intelligence (AI) machine learning algorithms with electrocatalyst synthesis techniques, successfully developing a new process optimization model that significantly enhances hydrogen generation efficiency through water splitting. Their breakthrough has been published in the prestigious international journal Journal of Materials Chemistry A and was featured as a cover article in the issue.
This study is part of the National Science and Technology Council (NSTC) Frontier AI Research Program, under the “Thematic Research Group Project.” The interdisciplinary team includes Professor Ming-De Yang, Dean of the College of Engineering; Professor Chih-Ming Chen, Vice Dean; Professor Hung-Chung Lee; and Professor Hou-Chien Chang, with contributions from postdoctoral researcher Chandrasekaran Pitchai and master’s student Ting-Yu Lo.
The team developed a new material, vanadium-doped nickel-cobalt layered double hydroxide (NiCoV LDHs), and used an AI model to analyze parameters such as material composition, electrolyte concentration, and synthesis temperature. The model accurately predicted the optimal synthesis and reaction conditions. Under these AI-optimized conditions, the hydrogen production efficiency improved by 21.4% compared to the best prior experimental results, with only a 6.1% deviation between model prediction and experimental verification — demonstrating remarkable precision. Further analyses showed that the new material offers superior electrical conductivity and long-term stability during reactions.
Notably, the AI system required fewer than 50 experimental data sets to predict nearly one million possible outcomes, reducing time and material costs by over 99% compared to traditional trial-and-error approaches.
Professor Chih-Ming Chen emphasized that this technology not only accelerates new material development but also helps future scientists minimize repetitive experimentation in energy material design. “This research demonstrates that AI can do far more than write essays, play chess, or chat — it can drive breakthroughs in energy science and help humanity transition faster toward a sustainable green energy future,” Chen said.
NCHU Team Advances Hydrogen Energy Research with Artificial Intelligence — Findings Featured in Journal of Materials Chemistry A
