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Deep learning (DL) has indeed emerged as a powerful tool for rapidly andaccurately predicting materials properties from big data, such as the design ofcurrent commercial Li-ion batteries. However, its practical utility formultivalent metal-ion batteries (MIBs), the most promising future solution oflarge-scale energy storage, is limited due to the scarce MIB data availabilityand poor DL model interpretability. Here, we develop an interpretable DL modelas an effective and accurate method for learning electrode voltages ofmultivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) atsmall dataset limits (150~500). Using the experimental results as validation,our model is much more accurate than machine-learning models which usually arebetter than DL in the small dataset regime. Besides the high accuracy, ourfeature-engineering-free DL model is explainable, which automatically extractsthe atom covalent radius as the most important feature for the voltage learningby visualizing vectors from the layers of the neural network. The presentedmodel potentially accelerates the design and optimization of multivalent MIBmaterials with fewer data and less domain-knowledge restriction, and isimplemented into a publicly available online tool kit inhttp://batteries.2dmatpedia.org/ for the battery community.

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