Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent.

ABSTRACT: Electrochromic devices have demonstrated considerable potential in a range of applications, including smart windows and automotive rearview mirrors. However, traditional cycle life testing methods are time-consuming and require significant resources to process a substantial amount of generated data, which presents a significant challenge and remains an urgent issue to be addressed. To address this challenge, we proposed the use of Long Short-Term Memory (LSTM) networks to construct a prediction model of the cycle life of electrochromic devices and introduced an interpretable analysis method to further analyze the model's predictive capabilities. The original dataset used for modeling was derived from preliminary experiments conducted under 1000 cycles of six devices prepared with varying m

SUBMITTER: Wu Y 

PROVIDER: S-EPMC11433885 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature