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arXiv · 2109.10890

Machine-Learning Enabled Search for The Next-Generation Catalyst for Hydrogen Evolution Reaction

S. Wei·S. Baek·H. Yue·S. Yun·S. Park·Y. Lee·J. Zhao·H. Li·K. Reyes·F. Yao
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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 (MoS$_2$) has been discovered recently with good activity and stability for HER. In this report, we employed the hydrothermal technique for MoS$_2$ synthesis which is a cost-effective and environmentally friendly approach and has the potential for future mass production. 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. A strong correlation between the material structure and the HER performance has been observed. Moreover, machine-learning (ML) techniques were built and subsequently used within a Bayesian Optimization framework to validate the optimal parameter combinations for synthesizing high-quality MoS$_2$ catalyst within the limited parameter space. The model will be able to guide the wet chemical synthesis of MoS$_2$ and produce the most effective HER catalyst eventually.

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S. Wei, S. Baek, H. Yue, S. Yun, S. Park, Y. Lee, J. Zhao, H. Li, K. Reyes, F. Yao. 2021-09-22. Machine-Learning Enabled Search for The Next-Generation Catalyst for Hydrogen Evolution Reaction. https://arxiv.org/abs/2109.10890

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