  A significant step forward in Lithium-ion batteries (LIBs) developments can
only be achieved by proposing mold-breaking research based on selecting the
best materials for the cell components, optimizing cell manufacture,
anticipating the degradation mechanisms of the LIBs, and consolidating the
regeneration processes of damaged batteries. LIBs with longer recycling life,
better safety, and the ability to be reused will establish sustainable
state-of-the-art batteries with maximum energy efficiency, low costs, and
minimal CO2 emissions within a circular economy, promoting sustainability in
areas as relevant as electromobility and portable electronics. Recently, there
has been increasing interest in applying Artificial Intelligence (AI)
techniques and their subclasses to better predict novel materials with designed
properties. This collection of methods has already obtained considerable
success, having been used to predict numerous physical properties of materials.
However, compared to other fields, the materials data are typically much
smaller and sometimes more diverse, which undoubtedly affects the construction
and effectiveness of AI models. At present, several Data Augmentation (DA)
methods have been proposed in materials science based on flipping, rotating,
and distorting the unit cells of materials, which have been demonstrated to be
very efficient in increasing the size and quality of data. Here we present an
even more effective new method of Data Augmentation based on the lattice
scaling of crystal structures. In the lattice scaling DA method, the unit cell
is perturbed, undergoing an increase and decrease of the unit cell volume in an
isotropic or anisotropic way. This transformation is particularly pertinent for
battery components since volume changes of up to 5% have been reported for the
insertion-based LIBs during cycling.
