It is over twenty years since the discovery of a giant magnetocaloric effect (GMCE) in Gd-Si-Ge, and in that time two low-cost GMCE materials systems have been pursued towardscommercialization: Mn-Fe-P-Si and La-Fe-Si . This relatively small number of target roomtemperature magnetic refrigerants underscores the need for time- and cost-effectivemagnetocaloric materials discovery projects. The ideal material candidates for magnetocaloricapplications must be non-toxic, inexpensive, and readily manufacturable . Highthroughputworkflows, guided by machine learning and other statistical methods in materialinformatics, are essential to streamlining the process of screening new possible materials,making the experimental process as targeted and efficient as possible. This presentationdescribes two approaches to materials screening based on simulated data.
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