期刊
JOURNAL OF MATERIALS CHEMISTRY A
卷 7, 期 32, 页码 19070-19080出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c9ta05453g
关键词
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资金
- EPSRC [EP/P022596/1]
- National Natural Science Foundation of China [2181101075, 91745103, 21621091]
- Leverhulme Early Career Fellowship
- Isaac Newton Trust
- EPSRC [EP/P022596/1] Funding Source: UKRI
Graphite and non-graphitising (hard) carbons are important anode materials for battery technologies. The electrochemical intercalation of alkali metals in graphite has been widely studied by first-principles density-functional theory (DFT). However, similar investigations of disordered hard and nanoporous carbons have been challenging due to the structural complexity involved. Here, we combine DFT with machine-learning (ML) methods to study the intercalation of alkali metal (Li, Na, K) atoms in model carbon systems over a range of densities and degrees of disorder. We use a stochastic approach to compute voltage-filling profiles, studying the three metal species side-by-side, and we analyse the ionic charges of metal atoms as a function of filling. Our study provides atomic-scale insight into the intercalation of all three alkali metals that are relevant to batteries, and it thereby makes a key step towards the DFT/ML-driven modelling of energy materials.
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