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Machine learning for data-driven design of high-safety lithium metal anode
- Source :
- STAR Protocols, Vol 5, Iss 1, Pp 102834- (2024)
- Publication Year :
- 2024
- Publisher :
- Elsevier, 2024.
-
Abstract
- Summary: 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).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
- Subjects :
- Chemistry
Energy
High Throughput Screening
Material sciences
Science (General)
Q1-390
Subjects
Details
- Language :
- English
- ISSN :
- 26661667
- Volume :
- 5
- Issue :
- 1
- Database :
- Directory of Open Access Journals
- Journal :
- STAR Protocols
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.40d480808bb9463da2b8cb668da04940
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.xpro.2023.102834