A bibliographic analysis of optimization of hydrogen production via electrochemical method using machine learning

The utilization of hydrogen energy is crucial for addressing environmental issues stemming from fuel catastrophes and greenhouse gas emissions. However, scientific advancements are still needed to determine the optimal technological paths for economical and eco-friendly hydrogen production. The paper presents a comprehensive bibliometric analysis of the evolving landscape of hydrogen evolution reaction and oxygen evolution reaction through electrochemical methods, with a specific focus on the optimization achieved through machine learning techniques. A corpus of 306 documents, comprising 276 articles and 33 reviews, spanning the years 2009 to 2023, was systematically extracted from the Web of Science database. Employing sophisticated bibliometric tools such as VOSviewer, Bibliometric, HistCite, and Giphe, an in-depth examination of the scholarly output was conducted in this domain. The analysis not only identifies key contributors shaping the field but also investigates the interdisciplinary connections that have played crucial roles in advancing the optimization of electrochemical hydrogen production. The review highlights the emergence of novel approaches, such as density functional theory coupled with neural networks for catalyst discovery, and support vector machine learning for optimizing small to medium size data set. In addition, a range of artificial intelligence techniques such as multilayer perceptron-artificial neural networking, adaptive neuro-fuzzy inference system, genetic algorithms -adaptive neuro-fuzzy inference system and gaussian process lead to the highest correlation and the lowest error for prediction of the hydrogen production. In brief, the study provides valuable insights into emerging trends and future research directions in the field of clean energy and sustainability.