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Predicting the capacitance of carbon-based electric double layer capacitors by machine learning.

Haiping SuSen LinShengwei DengCheng LianYazhuo ShangHonglai Liu
Published in: Nanoscale advances (2019)
Machine learning (ML) methods were applied to predict the capacitance of carbon-based supercapacitors. Hundreds of published experimental datasets are collected for training ML models to identify the relative importance of seven electrode features. This present method could be used to predict and screen better carbon electrode materials.
Keyphrases
  • machine learning
  • artificial intelligence
  • solid state
  • carbon nanotubes
  • deep learning
  • randomized controlled trial
  • gold nanoparticles
  • reduced graphene oxide