Chemically Controllable Magnetic Transition Temperature and Magneto‐Elastic Coupling in MnZnSb Compounds. (22nd February 2021) - Murgatroyd, Philip A. E.; Routledge, Kieran; Durdy, Samantha; Gaultois, Michael W.; Surta, T. Wesley; Dyer, Matthew S.; Claridge, John B.; Savvin, Stanislav N.; Pelloquin, Denis; Hébert, Sylvie; Alaria, Jonathan
Abstract: Magneto‐caloric materials offer the possibility to design environmentally friendlier thermal management devices compared to the widely used gas‐based systems. The challenges to develop this solid‐state based technology lie in the difficulty of finding materials presenting a large magneto‐caloric effect over a broad temperature span together with suitable secondary application parameters such as low heat capacity and high thermal conductivity. A series of compounds derived from the PbFCl structure is investigated using a combination of computational and experimental methods focusing on the change of cell volume in magnetic and non‐magnetic ground states. Scaling analysis of the magnetic properties determines that they are second order phase transition ferromagnets and that the magnetic entropy change is driven by the coupling of magneto‐elastic strain in the square‐net through the magnetic transition determined from neutron and synchrotron X‐ray diffraction. The primary and secondary application related properties are measured experimentally, and the c/a parameter is identified as an accurate proxy to control the magnetic transition. Chemical substitution on the square‐net affords tuning of the Curie temperature over a broad temperature span between 252 and 322 K. A predictive machine learning model for the c/a parameter is developed to guide future exploratory synthesis. Abstract : A combination of experimental and computational tools is used to identify magneto‐elastic coupling as driving mechanism for the magneto‐caloric effect (MCE) observed in MnZnSb. Chemical flexibility of the square net is used to control the Curie temperature ( T C ) and broaden the MCE over a wide temperature range. A machine learning model predicting the c / a crystallographic parameter is developed to guide exploratory synthesis.