Machine-Learning Assisted Exploration: Toward the Next-Generation Catalyst for Hydrogen Evolution Reaction. Issue 12 (22nd December 2021)

• Record Type: Journal Article
• Authors: Wei, Sichen; Baek, Soojung; Yue, Hongyan; Liu, Maomao; Yun, Seok Joon; Park, Sehwan; Lee, Young Hee; Zhao, Jiong; Li, Huamin; Reyes, Kristofer; Yao, Fei
• Is Part Of: Journal of the Electrochemical Society. Volume 168:Issue 12(2021)
• Publication Date: 2021-12-22
• Subjects: Electrochemistry -- Periodicals; 541.3705
• DOI: 10.1149/1945-7111/ac41f1
• Languages: English
• ISSNs: 0013-4651

Abstract

Abstract : The development of active catalysts for hydrogen evolution reaction (HER) made from low-cost materials constitutes a crucial challenge in the utilization of hydrogen energy. Earth-abundant molybdenum disulfide (MoS2 ) has been discovered recently with good activity and stability for HER. In this report, we employ a hydrothermal technique for MoS2 synthesis which is a cost-effective and environmentally friendly approach and has the potential for future mass production. Machine-learning (ML) techniques are built and subsequently used within a Bayesian Optimization framework to validate the optimal parameter combinations for synthesizing high-quality MoS2 catalyst within the limited parameter space. Compared with the heavy-labor and time-consuming trial-and-error approach, the ML techniques provide a more efficient toolkit to assist exploration of the most effective HER catalyst in hydrothermal synthesis. To investigate the structure-property relationship, scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and various electrochemical characterizations have been conducted to investigate the superiority of the ML validated optimized sample. A strong correlation between the material structure and the HER performance has been observed for the optimized MoS2 catalyst.