General approach for efficient prediction of refrigeration performance in caloric materials

Solid-state refrigeration holds promise for environmentally friendly cooling with high energy efficiency and downsize scalability. However, its refrigeration performance is notably inferior in comparison with commercial refrigerants due to the lack of scientific guidance for material discovery and performance improvement. Here, we provide an efficient approach to investigate caloric effect under external factors (i.e., electric fields, magnetic fields, and mechanical fields). Using the electrocaloric material ${\mathrm{Pb}\mathrm{Ti}\mathrm{O}}_{3}$ (PTO) as a prototype and employing ab initio calculations combined with deep potential machine learning, we demonstrate the field-dependent isothermal entropy change \ensuremath{\Delta}S and adiabatic temperature change \ensuremath{\Delta}T, along with the coefficient of performance near the phase transitions temperature (${T}_{c}$). Through analysis of the evolution of microscopic dynamics, we clarify that the refrigeration process involves heat absorption by a transition from a low-potential-energy to a high-potential-energy state. This transition, in a more general case, can be defined as a conventional caloric effect driven by temperature when T > ${T}_{c}$, or an inverse caloric effect driven by external field when T ${T}_{c}$. Importantly, this approach is successfully applied to magnetocaloric, elastocaloric, and barocaloric materials, from which we further showcase the regulation of caloric effect in multicaloric refrigeration process. This work establishes an effective and universal method for predicting key refrigeration parameters, which can be applied to extensive caloric materials, that provide important insights for material design in solid-state cooling technology.