  Machine learning (ML) techniques have rapidly found applications in many
domains of materials chemistry and physics where large data sets are available.
Aiming to accelerate the discovery of materials for battery applications, in
this work, we develop a tool (http://se.cmich.edu/batteries) based on ML models
to predict voltages of electrode materials for metal-ion batteries. To this
end, we use deep neural network, support vector machine, and kernel ridge
regression as ML algorithms in combination with data taken from the Materials
Project Database, as well as feature vectors from properties of chemical
compounds and elemental properties of their constituents. We show that our ML
models have predictive capabilities for different reference test sets and, as
an example, we utilize them to generate a voltage profile diagram and compare
it to density functional theory calculations. In addition, using our models, we
propose nearly 5,000 candidate electrode materials for Na- and K-ion batteries.
We also make available a web-accessible tool that, within a minute, can be used
to estimate the voltage of any bulk electrode material for a number of
metal-ions. These results show that ML is a promising alternative for
computationally demanding calculations as a first screening tool of novel
materials for battery applications.
