Elastocaloric Thermal Battery: Ultrahigh Heat-Storage Capacity Based on Generative Learning-Designed Phase-Change Alloys.

Abstract

An elastocaloric thermal battery based on generative learning-designed phase-change alloys is developed to facilitate the efficient recycling of low-temperature waste heat. This battery stores thermal energy as latent heat in a phase-change alloy and releases it on demand through applied stress at ambient temperature. Alloy compositions and corresponding processing parameters, tailored to desired transformation characteristics, are efficiently discovered through a generative learning-enabled inverse design framework, which converts the hand-drawn target heat flow curve into tangible compositional and processing designs. The designed battery achieves an ultrahigh figure of merit for heat storage capacity, surpassing existing thermal batteries, and boasts a work-to-heat efficiency exceeding 9. This opens up exciting possibilities for manipulating thermal energy in diverse applications such as low-temperature waste heat recycling, solar thermal collection, and heat management in electric vehicles and data center facilities. The inverse design framework promises to expedite the development of various materials with tailored property curves.