| Abstract Scope |
Hard magnetic materials are ubiquitous and, are used in a myriad of applications including, but not limited to computers, green energy technologies, and defense systems. Over the years, a variety of hard magnetic materials were developed to cater to the immanent technological demands. In the recent past, materials informatics has been an essential component of materials discovery, design, and development. We present a methodology that combines various multiple attribute decision-making methods, hierarchical clustering, and principal component analysis for data-driven hard magnetic materials selection. Shannon’s entropy model evaluated the relative weights of various properties followed by the ranking of the hard magnetic materials by the various multiple attribute decision making methods. Akin to Ashby charts, two-dimensional plots were developed to provide a visual presentation, based on the decision-making models, clustering, and component analysis followed by the assessment of the predictive capability of the data-driven model. |