Artificial intelligence-navigated development of high-performance electrochemical energy storage systems through feature engineering of multiple descriptor families of materials-Reference-Cited by-同舟云学术

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

Published:2023 Issue:5 Volume:2 Page:615-645
ISSN:2753-1457
Container-title:Energy Advances
language:en
Short-container-title:Energy Adv.

Author:

Adamu Haruna¹²³,Abba Sani Isah⁴,Anyin Paul Betiang⁵,Sani Yusuf⁶,Qamar Mohammad¹^ORCID

Affiliation:

1. Interdisciplinary Research Center for Hydrogen and Energy Storage (IRC-HES), King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

2. Department of Environmental Management Technology, ATBU, Bauchi, Nigeria

3. Department of Chemistry, ATBU, Bauchi, Nigeria

4. Interdisciplinary Research Center for Membrane and Water Security, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

5. Faculty of Engineering, Department of Computer Engineering, Ahmadu Bello University, Zaria, Nigeria

6. School of Computing and Communications InfoLab 21, Lancaster University, Lancaster LA1 4WA, UK

Abstract

With increased awareness of artificial intelligence-based algorithms coupled with the non-stop creation of material databases, artificial intelligence (AI) can facilitate fast development of high-performance electrochemical energy storage systems (EESSs).

Funder

King Fahd University of Petroleum and Minerals

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/YA/D3YA00104K

Reference253 articles.

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5. Emerging electrochemical energy conversion and storage technologies

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