Antiferroelectric thin films are demonstrated as a new class of giant electrocaloric materials that exhibit a negative electrocaloric response of about -5 K near room temperature. The giant negative electrocaloric effect may open up a new paradigm for light, compact, reliable, and high-efficiency refrigeration devices.

The critical dimension for reverse domain nucleation in the ferroelectric polarization switching of Pb͑Zr 0.4 Ti 0.6 ͒O 3 ͑PZT͒ thin film was estimated experimentally from a coercive voltage estimation in Pt/ Al 2 O 3 / PZT/ Ir ferroelectric thin-film capacitors with various Al 2 O 3 thicknesses and switching currents. The critical nuclei dimension for reverse domain formation in a 300-nm-thick PZT was only 4.5Ϯ 0.4 nm, which is in agreement with theoretical predictions of the critical nucleus size. Almost all the coercive voltage was applied to the nucleation layer thickness during ferroelectric switching. The classical Merz's exponential law for the domain velocity description and Laudauer's paradox of an implausibly large nucleation energy barrier were understood in terms of the charge-injection limited domain motion described by thermionic field emission at the Pt/ Al 2 O 3 interface or Fowler-Nordheim tunneling at the Pt/PZT interface.

Multilayer optical film plays a significant role in broad fields of optical application. Due to the nonlinear relationship between the dispersion characteristics of optical materials and the actual performance parameters of optical thin films, it is challenging to optimize optical thin film structure with the traditional models. In this paper, we present an implementation of Deep Q-learning, which suited for the most part for optical thin film. As a set of concrete demonstrations, we optimize solar absorber. The optimal program could optimal this solar absorber in 500 epoch (about 200 steps per-epoch) without any human intervention. Search results perform better than researchers’ manual searches.