Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries

By M. A. Shandiz, R. Gauvin. 2016

137 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/7d380c604dbdd14dae48d806ab4c6668c6e83067

Citations in the corpus

• Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design (2019, unknown): “Three major crystal types (i.e., monoclinic, orthorhombic, and tri-clinic) of silicate-based cathodes were predicted by Shandiz and Gauvin [83] using different classification algorithms.”