Machine Learning-Assisted High-Donor-Number Electrolyte Additive Screening toward Construction of Dendrite-Free Aqueous Zinc-Ion Batteries

The utilization of electrolyte additives has been regarded as an efficient strategy to construct dendrite-free aqueous zinc-ion batteries (AZIBs). However, the blurry screening criteria and time-consuming experimental tests inevitably restrict the application prospect of the electrolyte additive strategy. With the rise of artificial intelligence technology, machine learning (ML) provides an avenue to promote upgrading of energy storage devices. Herein, we proposed an intriguing ML-assisted method to accelerate the development efficiency of electrolyte additives on dendrite-free AZIBs. Concretely, we selected the Gutmann donor number (DN value) as a screen parameter, which can reflect the interaction between…

• NN
  National Natural Science Foundation of China

  Awards: 22008193, 52106110, 62474026, 52021004, 52173235
• XU
  Xi'an University of Architecture and Technology

  Award: 196032407
• VA
  Venture and Innovation Support Program for Chongqing Overseas Returnees
• NK
  National Key Research and Development Program of China

  Award: 2022YFB3803300