Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems

• Published in: Journal of computational science Vol. 66; p. 101900
• Main Authors: Sgura, Ivonne, Mainetti, Luca, Negro, Francesco, Quarta, Maria Grazia, Bozzini, Benedetto
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.01.2023
• Subjects: Battery modeling; Convolutional Neural Networks (CNN); Deep Learning; Parameter estimation; Reaction-diffusion PDEs; Turing patterns; Reaction-diffusion PDEs; Parameter estimation; Convolutional Neural Networks (CNN); Battery modeling; Deep Learning; Turing patterns

High-energy density metal anodes are a key solution for next-generation mobility batteries, but the difficulty of studying materials in real-life battery context leads to a methodological gap between theory and experiments, translating into poor device control. Imaging and spectroscopy are the ultim...