  Deep learning (DL) has indeed emerged as a powerful tool for rapidly and
accurately predicting materials properties from big data, such as the design of
current commercial Li-ion batteries. However, its practical utility for
multivalent metal-ion batteries (MIBs), the most promising future solution of
large-scale energy storage, is limited due to the scarce MIB data availability
and poor DL model interpretability. Here, we develop an interpretable DL model
as an effective and accurate method for learning electrode voltages of
multivalent MIBs (divalent magnesium, calcium, zinc, and trivalent aluminum) at
small dataset limits (150~500). 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. 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. The presented
model potentially accelerates the design and optimization of multivalent MIB
materials with fewer data and less domain-knowledge restriction, and is
implemented into a publicly available online tool kit in
http://batteries.2dmatpedia.org/ for the battery community.
