Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials

With the increased and rapid development of artificial intelligence-based algorithms coupled with the non-stop creation of material databases, artificial intelligence (AI) has played a great role in the development of high-performance electrochemical energy storage systems (EESSs). The development of high-performance EESSs requires the alignment of multiple properties or features of active materials of EESSs, which is currently achieved through experimental trial and error approaches that are tedious and laborious. In addition, they are considered costly, time-consuming and destructive. Hence, machine learning (ML), a crucial segment of AI, can readily accelerate the processing of feature- or property-performance characteristics of the existing and emerging chemistries and physics of active materials for the development of high-performance EESSs. Towards this direction, in this perspective, we present insight into how feature engineering can handle multiple feature/descriptor families of active materials of EESSs.