Issue 5, 2023

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

Abstract

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.

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

Article information

Article type
Perspective
Submitted
07 mrt 2023
Accepted
27 mrt 2023
First published
13 apr 2023
This article is Open Access
Creative Commons BY license

Energy Adv., 2023,2, 615-645

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

H. Adamu, S. I. Abba, P. B. Anyin, Y. Sani and M. Qamar, Energy Adv., 2023, 2, 615 DOI: 10.1039/D3YA00104K

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