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Status: Unchecked ×Keyword: aluminum batteries ×Clear all

2 claims from 1 paper

1. Materials Science › Machine Learning in Materials Science

   Interpretable learning of voltage for electrode design of multivalent metal-ion batteries

   Zhang, Jun, Jing and Lei · arXiv (Cornell University) · 2022

   The authors built an interpretable deep learning model that predicts electrode voltages of multivalent metal-ion batteries from small datasets, and offer it as an online tool for battery researchers.

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   1. Unchecked“Using the experimental results as validation, our model is much more accurate than machine-learning models which usually are better than DL in the small dataset regime.”
   2. UncheckedThe authors' deep learning model for battery voltage needs no hand-picked features and, by inspecting its layers, picks out atom covalent radius as the key feature.“Besides the high accuracy, our feature-engineering-free DL model is explainable, which automatically extracts the atom covalent radius as the most important feature for the voltage learning by visualizing vectors from the layers of the neural network.”