Haotian Chen

Schmidt AI in Science Fellow

Michigan Institute for Data Science

The vast amount of CO2 released by commercial aviation (e.g., 900 million tons in 2019) at high altitude significantly increased radiative forcing by trapping more heat on Earth’s atmosphere and accelerated global warming and ocean acidification. Decarbonizing aviation thus becomes the frontier of energy storage device research to discover materials with high specific energy/capacity, durable for fast and deep charging/discharging, and safe at extreme conditions, where electrolyte plays a vital role for these properties. Here, we propose the novel approach of robotic and in silico discovery of electrolytes guided by differentiable machine learning, to find the Pareto optimal electrolyte system for and beyond aviation batteries.

  • Science Mentor: Venkat Viswanathan, Aerospace Engineering, College of Engineering
  • AI Mentor: Alexander Rodríguez, Computer Science and Engineering, College of Engineering
  • Research Theme: Power systems (batteries) for aviation