Machine learning assisted design for active cathode materials

Sihan Yong, Zhuoyuan Zheng, Pingfeng Wang, Yumeng Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The traditional way of designing materials, including experimental measurement and computational simulation, are not efficient. Machine learning is considered a promising solution for material design in the recent years. By observing from previous data, machine learning finds patterns, learns from the patterns and predict the material properties. In this study, machine learning methods are used for discovering new cathode with better properties, includes crystal system learning and the property prediction. K-Folder cross-validation is used for finding the best training data with a limited dataset, nevertheless increasing the percentage of training data would ultimately result in better performance on prediction. It is found that, random forest gives the highest average accuracy in crystal system classification, meanwhile, extra randomized tree algorithm provides a higher averaged coefficient of determination and lower mean squared error in the regression model predicting electrical properties of cathodes. The random forest algorithm is chosen from a wide range of machine learning algorithms with the implementation of Monte Carlo validation. Based on the feature importance evaluation, oxygen contents are found to have the highest effects in determining capacity gravity and volume change in properties prediction.

Original languageEnglish (US)
Title of host publicationAdvanced Materials
Subtitle of host publicationDesign, Processing, Characterization, and Applications
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791884508
StatePublished - 2020
EventASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020 - Virtual, Online
Duration: Nov 16 2020Nov 19 2020

Publication series

NameASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)


ConferenceASME 2020 International Mechanical Engineering Congress and Exposition, IMECE 2020
CityVirtual, Online


  • Cathode material design
  • Classification
  • Crystal structure
  • Machine learning
  • Regression

ASJC Scopus subject areas

  • Mechanical Engineering


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