Unbeom Baeck, Min-Cheol Kim, Duong Nguyen Nguyen, Jaekyum Kim, Jaehyoung Lim, Yujin Chae, Namsoo Shin, Heechae Choi, Joon Young Kim, Chan-Hwa Chung, Woo-Seok Choe, Ho Seok Park, Uk Sim, Jung Kyu Kim
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引用次数: 0
Abstract
Back cover image: The rational design of transition metal incorporated electrocatalyst for hydrogen evolution reaction is an effective way to produce economical hydrogen. However, the practical application of data-driven methodology is limited due to the complexity of electrochemical systems. In article number cey2.70006, Kim and Sim et al. present the machine learning based facile strategy to optimize the catalyst and experimental conditions. The trained model accurately predicts experimental variables, which are validated by proton exchange membrane-based water electrolysis system. This work provides insight into the simplified approach for the design optimization of machine learning-assisted catalysts and systems.
期刊介绍：
Carbon Energy is an international journal that focuses on cutting-edge energy technology involving carbon utilization and carbon emission control. It provides a platform for researchers to communicate their findings and critical opinions and aims to bring together the communities of advanced material and energy. The journal covers a broad range of energy technologies, including energy storage, photocatalysis, electrocatalysis, photoelectrocatalysis, and thermocatalysis. It covers all forms of energy, from conventional electric and thermal energy to those that catalyze chemical and biological transformations. Additionally, Carbon Energy promotes new technologies for controlling carbon emissions and the green production of carbon materials. The journal welcomes innovative interdisciplinary research with wide impact. It is indexed in various databases, including Advanced Technologies & Aerospace Collection/Database, Biological Science Collection/Database, CAS, DOAJ, Environmental Science Collection/Database, Web of Science and Technology Collection.
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