Machine learning aided synthesis and screening of HER catalyst: Present developments and prospects

Material Engineering Strategies for Efficient Hydrogen Evolution Reaction Catalysts

Water electrolysis, a key enabler of hydrogen energy production, presents significant potential as a strategy for achieving net-zero emissions. However, the widespread deployment of water electrolysis is currently limited by the high-cost and scarce noble metal electrocatalysts in hydrogen evolution reaction (HER). Given this challenge, design and synthesis of cost-effective and high-performance alternative catalysts have become a research focus, which necessitates insightful understandings of HER fundamentals and material engineering strategies. Distinct from typical reviews that concentrate only on the summary of recent catalyst materials, this review article shifts focus to material engineering strategies for developing efficient HER catalysts. In-depth analysis of key material design approaches for HER catalysts, such as doping, vacancy defect creation, phase engineering, and metal-support engineering, are illustrated along with typical research cases. A special emphasis is placed on designing noble metal-free catalysts with a brief discussion on recent advancements in electrocatalytic water-splitting technology. The article also delves into important descriptors, reliable evaluation parameters and characterization techniques, aiming to link the fundamental mechanisms of HER with its catalytic performance. In conclusion, it explores future trends in HER catalysts by integrating theoretical, experimental and industrial perspectives, while acknowledging the challenges that remain.

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting

Green hydrogen produced via electrochemical water splitting is a suitable candidate to replace emission-intensive fuels. However, the successful widespread adoption of green hydrogen is contingent on the development of low-cost, earth-abundant catalysts. Herein, machine learning models built on experimental data were used to optimize the precursor ratios of hydroxide-based electrocatalysts, with the objective of improving the product's electrocatalytic performance for overall water splitting. The Neural Network-based models were found to be the most effective in predicting and minimizing the overpotentials of the catalysts, reaching a minimum in two iterations. The relatively mild reaction conditions of the synthesis procedure, coupled with its scalability demonstrated herein, renders the optimized catalyst relevant for industrial implementation in the future. The optimized catalyst, characterized to be a molybdate-intercalated CoFe LDH, demonstrated overpotentials of 266 and 272 mV at 10 mA cm-2 for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.

Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction

Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water. Nevertheless, the conventional "trial and error" method for producing advanced electrocatalysts is not only cost-ineffective but also time-consuming and labor-intensive. Fortunately, the advancement of machine learning brings new opportunities for electrocatalysts discovery and design. By analyzing experimental and theoretical data, machine learning can effectively predict their hydrogen evolution reaction (HER) performance. This review summarizes recent developments in machine learning for low-dimensional electrocatalysts, including zero-dimension nanoparticles and nanoclusters, one-dimensional nanotubes and nanowires, two-dimensional nanosheets, as well as other electrocatalysts. In particular, the effects of descriptors and algorithms on screening low-dimensional electrocatalysts and investigating their HER performance are highlighted. Finally, the future directions and perspectives for machine learning in electrocatalysis are discussed, emphasizing the potential for machine learning to accelerate electrocatalyst discovery, optimize their performance, and provide new insights into electrocatalytic mechanisms. Overall, this work offers an in-depth understanding of the current state of machine learning in electrocatalysis and its potential for future research.

In Situ Surface Reconstruction of Catalysts for Enhanced Hydrogen Evolution

The in situ surface reconstitution of a catalyst for hydrogen evolution refers to its structure evolution induced by strong interactions with reaction intermediates during the hydrogen evolution reaction (HER), which eventually leads to the self-optimization of active sites. In consideration of the superior performance that can be achieved by in situ surface reconstitution, more and more attention has been paid to the relationship between active site structure evolution and the self-optimization of HER activity. More and more in situ and/or operando techniques have been explored to track the dynamic structural evolution of HER catalysts in order to clarify the underlying mechanism. This review summarizes recent advances in various types of reconstruction such as the reconfiguration of crystallinity, morphological evolution, chemical composition evolution, phase transition refactoring, surface defects, and interface refactoring in the HER process. Finally, different perspectives and outlooks are offered to guide future investigations. This review is expected to provide some new clues for a deeper understanding of in situ surface reconfiguration in hydrogen evolution reactions and the targeted design of catalysts with desirable structures.