A data-driven interpretable method to predict capacities of metal ion doped TiO<sub>2</sub> anode materials for lithium-ion batteries using machine learning classifiers
Mingxi Jiang, Yajuan Zhang, Zihao Yang, Haibo Li, Jinliang Li, Jiabao Li, Ting Lu, Chenglong Wang, Guang Yang, Likun Pan
Abstract
Machine learning classifier models were built with the datasets of different ions doped into TiO 2 materials to predict their charging and discharging performance.
Topics & Concepts
AnodeMaterials scienceIonLithium (medication)DopingLithium metalMetalAnalytical Chemistry (journal)Inorganic chemistryOptoelectronicsMetallurgyChemistryPhysical chemistryChromatographyElectrodeEndocrinologyOrganic chemistryMedicineAdvancements in Battery MaterialsAdvanced Battery Technologies ResearchAdvanced Battery Materials and Technologies