Machine learning-assisted materials development and device management in batteries and supercapacitors: performance comparison and challenges Academic Article

abstract

• This review compares machine learning approaches for property prediction of materials, optimization, and energy storage device health estimation. Current challenges and prospects for high-impact areas in machine learning research are highlighted.

authors

• Liang, Hong

published proceedings

• JOURNAL OF MATERIALS CHEMISTRY A

altmetric score

• 2

author list (cited authors)

• Jha, S., Yen, M., Salinas, Y. S., Palmer, E., Villafuerte, J., & Liang, H.

citation count

• 0

complete list of authors

• Jha, Swarn||Yen, Matthew||Salinas, Yazmin Soto||Palmer, Evan||Villafuerte, John||Liang, Hong

publication date

• February 2023

publisher

• Royal Society of Chemistry (RSC)  Publisher

published in

• Journal of Materials Chemistry A  Journal

keywords

• 34 Chemical Sciences
• 40 Engineering
• 4016 Materials Engineering
• 7 Affordable And Clean Energy
• Bioengineering

Digital Object Identifier (DOI)

• 10.1039/d2ta07148g

start page

• 3904

end page

• 3936

volume

• 11

issue

• 8

URL

• http://dx.doi.org/10.1039/d2ta07148g