Machine learning assisted investigation of the barocaloric performance in ammonium iodide

Using the ab initio-based training database, we trained potential function for ammonium iodide (NH4I) based on a deep neural network-based model. On basis of this function, simulated temperature-driven β ⇒ α-phase transition NH4I with isobaric isothermal ensemble via molecular dynamics simulations, results which are in good agreement recent experimental results. As it increases near phase temperature, quarter ionic bonds NH4+-I− break so that NH4+ starts to rotate randomly disorderly manner, being able store thermal energy without temperature rise. It is found possesses giant entropy change (∼93 J K−1 kg−1) and adiabatic (∼27 K) at low driving pressure (∼10 MPa). In addition, through partial substitution I by Br NH4I, conductivity can be remarkably improved, ascribed enhancement lifetime frequency phonons contributed bromine iodine. The present work provides method important guidance future exploration design barocaloric material practical applications.