Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90247
PIRA download icon_1.1View/Download Full Text
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 SizeFormat 
1-s2.0-S2666546821000033-main.pdf10.31 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

50
Citations as of May 22, 2022

Downloads

27
Citations as of May 22, 2022

SCOPUSTM   
Citations

21
Citations as of May 27, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.