Capacity-prediction models for organic anode-active materials of lithium-ion batteries: advances in predictors using small data
Haruka Tobita, Yuki Namiuchi, Takumi Komura, Hiroaki Imai, Koki Obinata, Masato Okada, Yasuhiko Igarashi, Yuya Oaki
Abstract
A capacity prediction model for organic anode active materials was constructed using sparse modeling for small data. The new model was validated in terms of the prediction accuracy, validity of the descriptors, and amount of the training data.
Topics & Concepts
AnodeLithium (medication)Predictive modellingComputer scienceMaterials scienceIonData miningMachine learningChemistryElectrodeMedicineEndocrinologyPhysical chemistryOrganic chemistryMachine Learning in Materials ScienceAdvanced Battery Technologies ResearchAdvancements in Battery Materials