Using machine learning to screen non-graphite carbon materials based on Na-ion storage properties-Reference-Cited by-同舟云学术

Using machine learning to screen non-graphite carbon materials based on Na-ion storage properties

Published:2022 Issue:14 Volume:10 Page:8031-8046
ISSN:2050-7488
Container-title:Journal of Materials Chemistry A
language:en
Short-container-title:J. Mater. Chem. A

Author:

Liu Xiaoxu¹^ORCID,Wang Tian¹,Ji Tianyi¹,Wang Hui²,Liu Hui¹^ORCID,Li Junqi¹^ORCID,Chao Dongliang³

Affiliation:

1. School of Material Science and Engineering, Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials, Shaanxi University of Science and Technology, Xi'an, 710021, China

2. Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore

3. Laboratory of Advanced Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Fudan University, Shanghai, 200433, China

Abstract

Study of non-graphite carbon materials for sodium ion batteries. A structure–property relationship database was analyzed and applied with machine learning.

Funder

National Natural Science Foundation of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Renewable Energy, Sustainability and the Environment,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/TA/D1TA10588D

Reference97 articles.

1. Dual-graphite cells based on the reversible intercalation of bis(trifluoromethanesulfonyl)imide anions from an ionic liquid electrolyte

2. Hierarchical porous hard carbon enables integral solid electrolyte interphase as robust anode for sodium-ion batteries

3. Hard Carbon Microtubes Made from Renewable Cotton as High-Performance Anode Material for Sodium-Ion Batteries