Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage

Abstract

The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself.