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Toward the Prediction of Electrochromic Properties of WO 3 Films: Combination of Experimental and Machine Learning Approaches.

Brandon FaceiraLionel Teule-GayGian Marco RignaneseAline Rougier
Published in: The journal of physical chemistry letters (2022)
WO 3 is the state of the art of electrochromic oxide materials finding technological application in smart windows. In this work, a set of WO 3 thin films were deposited by magnetron sputtering by varying total pressure, oxygen partial pressure, and power. On each film two properties were measured, the electrochemical reversibility and the blue color persistence of Li x WO 3 films in simulated ambient conditions. With the help of machine learning, prediction maps for such electrochromic properties, namely, color persistence and reversibility, were designed. High-performance WO 3 films were targeted by a global score which is the product of these two properties. The combined approach of experimental measurements and machine learning led to a complete picture of electrochromic properties depending of sputtering parameters providing an efficient tool in regards to time saving.
Keyphrases
  • machine learning
  • room temperature
  • artificial intelligence
  • big data
  • visible light
  • particulate matter
  • deep learning