Data-Driven Hard-Magnetic Material Selection for AC Applications by Multiple Attribute Decision Making

Abstract

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 material selection. Shannon’s entropy model evaluated the relative weights of multiple 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.