Enhancing energy materials with data-driven methods: a roadmap to long-term hydrogen energy sustainability using machine learning

In the past few years, there has been a lot of interest in studying new substances and figuring out how their structure affects their activity. This is seen as an alternative to the problems that come with traditional methods of making energy materials, like the high cost of computation, the time consumption, and the low success rate. Improving the study and production of energy materials requires new research, ideas, and methodologies. Some believe that data-driven materials science, enabled by recent advances in artificial intelligence (AI) and machine learning (ML), might modify current scientific knowledge and radically alter the production of energy materials. This includes essential advancements in hydrogen energy, like creating catalysts for producing hydrogen, finding materials for storing hydrogen, and improving fuel cell components. New findings in data-driven materials science suggest that ML technologies enable the development, identification and deployment of improved energy materials while simultaneously making their creation and improvement less of a hassle. This paper argues that funding research into alternative energy materials is an important first step towards achieving global carbon neutrality. Also included is a comprehensive ML concept overview covering topics like open-source databases, feature development, ML algorithms, and ML model assessment, among others. We discuss the modern developments in data-driven material sciences (DDMS) and technology, which cover topics such as materials for alkaline ion batteries, solar energy catalysis, and carbon dioxide recovery. This section concludes with some important ideas for making effective use of ML and some additional difficulties in creating new energy materials.