Abstract
Hydrogen is a clean energy carrier and has potential applications in energy storage, power generation, and transportation. This study explores the efficient and safe storage of hydrogen, particularly through solid-state methods using high entropy alloys (HEAs). HEAs have garnered attention for their versatility in tailoring properties for hydrogen storage. The integration of Machine Learning (ML) in designing HEAs offers an expedited approach, analyzing datasets and predicting material properties to enhance storage capacity, kinetics, and stability. Despite significant progress, the study acknowledges certain research limitations, particularly its relatively narrow focus on applying ML to HEAs for hydrogen storage. One of the biggest challenges with HEAs is their complexity, which.
necessitates larger datasets to develop accurate predictive models. Collecting and analyzing existing HEA data for hydrogen storage using ML techniques is the main objective. Using algorithms like support vector regression (SVR), K-nearest (KNN), and random forest (RF), hydrogen-to-metal ratio (H/M) and valence electron configuration (VEC) are accurately predicted. The study proposes alloys with potential for HEA formation, identifying 741 with quaternary and 631 with quinary HEAs. These compositions are newly proposed and do not yet exist. Out of these, 774 HEAs are identified as potential candidates for hydrogen storage applications. Applying ML techniques, the selection process is more efficient, reducing the dependency on time-consuming experiments and making it easier to discover promising candidates.
•Machine learning (ML) was used in designing high entropy alloys (HEA) for energy storage.•KNN stands out as the most effective algorithm, balancing precision and model complexity.•By leveraging ML techniques, a reduced pool of potential candidates by 50 % was attained.•The selection process minimized reliance on time-consuming experimental methods.