Braving the Elements: Learning from 60+ Dopants in Battery Materials

Abstract

The transition toward renewable energy sources is motivating a great deal of research into new secondary battery materials for important applications such as electric vehicles and grid storage. This research is generally very fragmented with each study only looking at a few compositions and each research team using their own methods/protocols, which greatly limits comparisons between studies. Recently, high-throughput methods have been developed and used to screen the impact of a very high number of dopants simultaneously (72 different elements at the latest count). These studies have focused on both electrodes in Liion batteries, Na-ion cathodes, and solid electrolytes for both Li and Na batteries. This large-data-driven research is highly efficient in generating advanced materials for practical devices, but it also provides a great opportunity to enhance our understanding of how substitutions impact the wide variety of intrinsic properties of importance for battery materials. Here, I summarize the key trends seen across these studies and provide a perspective of where this research is leading. This will include a discussion of progress toward a global understanding of how to predict whether a dopant will in fact dope into a structure (this has to date been poorly predicted by computational approaches) and also the potential to develop codoping strategies to optimize multiple key properties at once. Finally, opportunities to make use of these large data sets with artificial intelligence/machine-learning will be discussed. These will dramatically enhance our ability to rationally design advanced battery materials without limiting open exploration.