AI-enabled materials discovery for advanced ceramic electrochemical cells
Bello, Idris Temitope Taiwo, Ridwan Esan, Oladapo Christopher Adegoke, Adesola Habeeb Ijaola, Ahmed O. Li, Zheng Zhao, Siyuan Wang, Chen Shao, Zongping Ni, Meng
Bello, Idris Temitope
Taiwo, Ridwan
Esan, Oladapo Christopher
Adegoke, Adesola Habeeb
Ijaola, Ahmed O.
Li, Zheng
Zhao, Siyuan
Wang, Chen
Shao, Zongping
Ni, Meng
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2024-01
Type
Article
Genre
Keywords
Ceramic electrochemical cells,Artificial intelligence,Materials design,Materials optimization,Materials performance,Machine learning
Subjects (LCSH)
Citation
Bello, I.T., Taiwo, R., Esan, O.C., Adegoke, A.H., Ijaola, A.O., Li, Z., Zhao, S., Wang, C., Shao, Zongping, & Ni, M. (2024). AI-enabled materials discovery for advanced ceramic electrochemical cells. Energy and AI, vol.15, art. no. 100317. https://doi.org/10.1016/j.egyai.2023.100317
Abstract
Ceramic electrochemical cells (CECs) are promising devices for clean and efficient energy conversion and storage due to their high energy efficiency, more extended system durability, and less expensive materials. However, the search for suitable materials with desired properties, including high ionic and electronic conductivity, thermal stability, and chemical compatibility, presents ongoing challenges that impede widespread adoption and further advancement in the field. Artificial intelligence (AI) has emerged as a versatile tool capable of enhancing and expediting the materials discovery cycle in CECs through data-driven modeling, simulation, and optimization techniques. Herein, we comprehensively review the state-of-the-art AI applications for materials design and optimization for CECs, covering various material aspects, database construction, data pre-processing, and AI methods. We also present some representative case studies of AI-predicted and synthesized materials for CECs and provide insightful highlights about their approaches. We emphasize the main implications and contributions of the AI approach for advancing the CEC technology, such as reducing the trial-and-error experiments, exploring the vast materials space, discovering novel and optimal materials, and enhancing the understanding of the materials-performance relationships. We also discuss the AI approach's main limitations and future directions for CECs, such as addressing the data and model challenges, improving and extending the AI models and methods, and integrating with other computational and experimental techniques. We conclude by suggesting some potential applications and collaborations for AI in materials design for CECs.
Table of Contents
Description
Click on the DOI link to access this article (may not be free).
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Publisher
Elsevier B.V.
Journal
Book Title
Series
Energy and AI
v.15, art. no. 100317
v.15, art. no. 100317
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
DOI
ISSN
2666-5468