Machine learning assisted optimization of electrochemical properties for Ni-rich cathode materials.

ABSTRACT: Optimizing synthesis parameters is the key to successfully design ideal Ni-rich cathode materials that satisfy principal electrochemical specifications. We herein implement machine learning algorithms using 330 experimental datasets, obtained from a controlled environment for reliability, to construct a predictive model. First, correlation values showed that the calcination temperature and the size of the particles are determining factors for achieving a long cycle life. Then, we compared the accuracy of seven different machine learning algorithms for predicting the initial capacity, capacity retention rate, and amount of residual Li. Remarkable predictive capability was obtained with the average value of coefficient of determinant, R² = 0.833, from the extremely randomized tree

SUBMITTER: Min K 

PROVIDER: S-EPMC6202356 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature