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

作者：Kong, Sifan; Li, Muyun; Xiang, Yongqi; Wu, Yitong; Fan, Zhen^*; Yang, Huan^*; Cai, Qingyue; Zhang, Menglong; Zhang, Yong; Ning, Honglong^*

来源：Journal of Materials Chemistry C, 2023, 11(37): 12776-12784.

DOI：10.1039/d3tc02522e

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 ( ?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 ?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.