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• W3165171569 abstract "• Ensemble machine learning is applied to predict the specific capacity (~26.6 mAh g −1 ) and capacity retention of the material (>90%) under particular conditions. • Cerium oxynitride is synthesized using a urea glass method. • The experiment validates the prediction (~26 mAh g −1 and ~100% specific capacity retention) under the same conditions. From an engineering standpoint, specific capacity and cyclic stability may be considered the two most critical performance-related features for supercapacitor electrodes. The prediction of these two parameters is hence crucial for evaluating the prospect of a given material for a supercapacitor-electrode application. However, this prediction is highly non-trivial using existing atomistic approaches. As a solution, a combinatorial approach of value and grade prediction machine-learning models are used to predict the performance of a novel material (cerium oxynitride) for supercapacitor application. The model predicts the material to have a specific capacity of ~26.6 mAh g −1 and capacity retention of >90% for a particular material (morphology, composition, surface area) and operational (current density, applied potential window etc.) properties; which can be viably achieved via urea glass method. The experimental results (~26 mAh g −1 and ~100% capacity retention) considerably validate the predictive approach presented here. This article is the first instance wherein cerium oxynitride has been predicted and reported as a supercapacitor electrode. This makes the prediction and the validation made in this study of contemporary relevance." @default.
• W3165171569 created "2021-06-07" @default.
• W3165171569 creator A5059957211 @default.
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• W3165171569 date "2021-09-01" @default.
• W3165171569 modified "2023-10-13" @default.
• W3165171569 title "Machine learning-based prediction of supercapacitor performance for a novel electrode material: Cerium oxynitride" @default.
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• W3165171569 doi "https://doi.org/10.1016/j.ensm.2021.05.024" @default.
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