Machine learning for data-driven design of high-safety lithium metal anode

Publication date
2024-01-09
Document type
Sonstiger Artikeltyp
Author
Zhang, Qi
Dong, Junlin
Zhou, Chuan
Zhang, Dantong
Yuan, Shuguang
Xue, Dongfeng
Peng, Chao
Organisational unit
DOI
URI
Scopus ID
Publisher
Elsevier
Series or journal
STAR Protocols
ISSN
Periodical volume
5
Periodical issue
1
Article ID
102834
References
Part of the university bibliography
✅
Language
English
Keyword
Chemistry
Energy
High Throughput Screening
Material sciences
Abstract
Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open-sourced databases and calculating their microscopic properties. We then detail procedures for developing a machine learning model for predicting the ionic diffusion barrier and preparing the inputs for prediction. This protocol enables a cost-effective workflow to identify promising self-assembled monolayers with exceptional performance. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).
Description
Under a Creative Commons license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Version
Published version
Access right on openHSU
Metadata only access