Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron() chloride-heavy water electrochromic liquid

Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current. However, there is still a lack of an effective tool to guide the development of high-performance electrochromic devices. Here, we design a high-performance electrochromic device, which is composed of a mixed functional layer synthesized from ammonium metatungstate and iron( ii ) chloride in a heavy water solvent, with the aid of a multilayer perceptron (MLP) model. We first prepare 25 devices with different concentrations of ammonium metatungstate and iron( ii ) chloride, and use their transmittance modulation amplitude (Δ T ) current density data to train an MLP model. Then, this model is further used to guide the experimental fabrication of the best-performing device. The fabricated device exhibits high Δ T (74%), rapid response time ( t c = 6.5 s and t b = 13.5 s), and long cycling life (>1000), which represents a breakthrough in the field of inorganic all-liquid electrochromic devices. Our study showcases a new paradigm of developing high-performance electrochromic devices by using machine learning. Electrochromic devices have been widely studied due to their ability to change transmittance under the application of electrical current.