Machine-Learning Assisted Exploration: Toward the Next-Generation Catalyst for Hydrogen Evolution Reaction
Machine-Learning Assisted Exploration: Toward the Next-Generation Catalyst for Hydrogen Evolution Reaction
复制标题
机器学习辅助探索：迈向下一代析氢反应催化剂
DOI：
10.1149/1945-7111/ac41f1
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发表时间：
2021
影响因子：
3.9
通讯作者：
Reyes, Kristofer
中科院分区：
文献类型：
--
作者：
Wei, Sichen;Baek, Soojung;Yue, Hongyan;Liu, Maomao;Yun, Seok Joon;Park, Sehwan;Lee, Young Hee;Zhao, Jiong;Li, Huamin;Reyes, Kristofer
开发低成本材料的析氢反应活性催化剂是氢能利用的一个关键挑战。近年来，地球资源丰富的二硫化钼（MoS2）被发现具有良好的HER活性和稳定性，本文采用水热法合成MoS2，该方法具有成本低、环境友好的特点，具有大规模生产的潜力。机器学习（ML）技术的建立，并随后使用贝叶斯优化框架内，以验证在有限的参数空间内合成高品质的二硫化钼催化剂的最佳参数组合。与费时费力的试错法相比，ML技术提供了一个更有效的工具包，以帮助探索最有效的HER催化剂在水热合成。通过扫描电子显微镜（SEM）、透射电子显微镜（TEM）、X射线衍射（XRD）、拉曼光谱（Raman）、X射线光电子能谱（XPS）和各种电化学表征研究了ML验证优化样品的结构与性能的关系。优化的二硫化钼催化剂的材料结构和HER性能之间有很强的相关性。
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 employ a hydrothermal technique for MoS 2 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 MoS 2 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 MoS 2 catalyst.