Predicting Anion Redox in Secondary Battery Cathode Materials with a Data-Driven Model

In
this study, a new empirical model for predicting the likelihood
that a material will exhibit anion redox under cation intercalation
is developed with a machine learning approach, and many promising
new materials are predicted by applying the model to thousands of
candidates. This model is applied to a subset of the Inorganic Crystal
Structure Database to determine trends in reported literature materials
that can guide design and exploration of new materials that exhibit
anion redox to obtain high energy storage capacities without the pitfalls,
such as low cyclability, that plague known anion redox materials.
Anion redox cathodes improve the energy density of current lithium
and sodium secondary batteries owing to their ability to charge compensate
mobile cation insertion/extraction through changing the oxidation
state of the anion in addition to a transition-metal species. Although
the true mechanism through which anion charge compensation occurs
has not been fully elucidated, materials that exhibit this phenomenon
have recently become the topic of intense interest given their potential
to help improve the energy density of secondary batteries beyond current
capabilities. Anion close-packed structures and high-valent transition
metals are confirmed to be key attributes for enabling anion redox
in these materials.