Exploration and quantification of magnetocaloric effect in EuTiO3 perovskite using extreme learning machine intelligent computational method

Abstract

Perovskite EuTiO3 is a multi-ferroic titanate oxide material that exhibits huge magnetocaloric effect at low magnetic field without associated hysteresis loss due to the magnetic field and thermal energy. These unique features coupled with the presence of strong coupling existing between spin and lattice as well as the possibility to switch the magnetic ground state between ferromagnetism and anti-ferromagnetism, foster the potentials of this material for magnetocaloric effect based cooling technology application. Maximum magnetic entropy change of perovskite EuTiO3 determines the strength of magnetocaloric effect in the material and ultimately influences the usability of this material for cooling technology. However, experimental measurement of maximum magnetic entropy change is challenging and consumes appreciable resources and time. Maximum magnetic entropy change of intrinsic and doped EuTiO3 perovskite is modeled in this work using extreme learning machine intelligent algorithm (ELMIA) with the ionic radii, elemental concentration and applied magnetic field descriptors. The developed ELMIA-SINE with sine activation function performs better than ELMIA-SIG model with sigmoid activation function with performance superiority of 42.46%, 44.58% and 1.76% using root mean square error, mean absolute error and correlation coefficient performance yardstick, respectively. The developed ELMIA-SINE model further outperforms the existing particle swarm based support vector regression model with polynomial (PSVR-P) and Gaussian (PSVR-G) function with performance improvement of 37.03% and 14.11%, respectively. The dependence of applied magnetic field and dopant concentration on hugeness of magnetocaloric effect was established using the developed model. The precision and the ease of implementation of the developed ELMIA model as compared with the existing models would definitely facilitate exploration of magnetocaloric effect based system of refrigeration for addressing the present energy crisis.