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 employed the hydrothermal technique for MoS2 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 MoS2 catalyst within the limited parameter space. The model will be able to guide the wet chemical synthesis of MoS2 and produce the most effective HER catalyst eventually.
@article{arxiv.2109.10890,
title = {Machine-Learning Enabled Search for The Next-Generation Catalyst for Hydrogen Evolution Reaction},
author = {S. Wei and S. Baek and H. Yue and S. Yun and S. Park and Y. Lee and J. Zhao and H. Li and K. Reyes and F. Yao},
journal= {arXiv preprint arXiv:2109.10890},
year = {2021}
}