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 ultimate tools for knowledge-based battery studies, but the capability of quantitatively linking the electrical device response to the material evolution, is essentially missing, at the moment. High-throughput, Deep-Learning based parameter identification for morphochemical PDE modelling, can enable this link, in principle allowing to extract the key information from hyperspectral imaging tools and to feed it into next-generation battery management systems: this work is the first step of this process. Here we have employed a CNN, trained with the solutions of an electrochemical phase formation model we recently developed, to carry out three enabling tasks: (i) automatic partitioning of the parameter space, according to the types of patterns generated by the model; (ii) assignment of experimental patterns, derived by imaging of from real electrodes to the class patterns and (iii) identification of the model parameters for experimental electrode images.
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