DataVXCarbon EnergyPaper
journal article·2025-07Open access

Back Cover Image, Volume 7, Number 7, July 2025

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

Carbon Energy· Volume 7· Issue 7
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Source abstract

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.
Source: openalex_inverted_index · Quality: baseline

Research context

Catalysis & surfaces

OpenAlex topics: Machine Learning in Materials Science · Electrocatalysts for Energy Conversion · CO2 Reduction Techniques and Catalysts

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Chemical Engineering Sungkyunkwan University (SKKU) Suwon Republic of Korea

Department of Chemistry Sookmyung University Seoul Republic of Korea

Hydrogen Energy Technology Laboratory, Korea Institute of Energy Technology (KENTECH) Naju Republic of Korea

Deep Solution Inc. Gyeonggido Republic of Korea

Department of Chemistry Xi'an Jiaotong‐Liverpool University Suzhou Jiangsu China

Research Institute, NEEL Sciences Inc. Naju Republic of Korea

SKKU Institute of Energy Science and Technology (SIEST) Sungkyunkwan University (SKKU) Suwon Republic of Korea

SKKU Advanced Institute of Nano Technology (SAINT) Sungkyunkwan University Suwon Republic of Korea

References

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