Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/90247
| Title: | Machine learning for advanced energy materials | Authors: | Liu, Y Esan, OC Pan, Z An, L |
Issue Date: | Mar-2021 | Source: | Energy and AI, Mar. 2021, v. 3, 100049 | Abstract: | The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low success probabilities, high time consumption, and high computational cost associated with the traditional methods of developing energy materials. Following this, new research concepts and technologies to promote the research and development of energy materials become necessary. The latest advancements in artificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of energy materials. Furthermore, the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment. In this article, the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented. A comprehensive introduction to the fundamentals of machine learning is also provided, including open-source databases, feature engineering, machine learning algorithms, and analysis of machine learning model. Afterwards, the latest progress in data-driven materials science and engineering, including alkaline ion battery materials, photovoltaic materials, catalytic materials, and carbon dioxide capture materials, is discussed. Finally, relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted. | Keywords: | Energy materials Artificial intelligence Machine learning Data-driven materials science and engineering Prediction of materials properties Design and discovery of energy materials |
Publisher: | Elsevier BV | Journal: | Energy and AI | EISSN: | 2666-5468 | DOI: | 10.1016/j.egyai.2021.100049 | Rights: | © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/) The following publication Liu, Y., Esan, O. C., Pan, Z., & An, L. (2021). Machine learning for advanced energy materials. Energy and AI, 3, 100049 is available at https://doi.org//10.1016/j.egyai.2021.100049 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 1-s2.0-S2666546821000033-main.pdf | 10.31 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
72
Last Week
0
0
Last month
Citations as of Oct 1, 2023
Downloads
76
Citations as of Oct 1, 2023
SCOPUSTM
Citations
74
Citations as of Sep 28, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.