Machine learning-based design of electrocatalytic materials towards high-energy lithium||sulfur batteries development
Work
Year: 2024
Type: article
Abstract: The practical development of Li | |S batteries is hindered by the slow kinetics of polysulfides conversion reactions during cycling. To circumvent this limitation, researchers suggested the use of tra... more
Source: Nature Communications
Institutions Tsinghua–Berkeley Shenzhen Institute, Tsinghua University, Shanghai Jiao Tong University, Northwestern Polytechnical University
Cites: 53
Cited by: 4
Related to: 10
FWCI: 0.514
Citation percentile (by year/subfield): 63.15
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Sustainable Development Goal Affordable and clean energy
Open Access status: gold
APC paid (est): $4,808