Computational insight into the grain boundary structure and atomic mobility in metallic lithium

Sergeev, Artem V.; Rulev, Alexey A; Kondratyeva, Yevgeniya O.; Yashina, Lada V.

doi:10.1016/j.actamat.2022.117988

Cited by 6 publications

(1 citation statement)

References 68 publications

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“…(3) MD simulations to study the grain boundary (GB) structures of the polycrystalline metal anodes, Li diffusion through the GB structures, atomic-scale structure mismatch between Li metal and Cu current collectors, generation of vacancies [42]; (4) large-scale MD simulations to study the defects and pore formation within Li metals at the Li metal-solid state electrolyte [43]; (5) AIMD simulations to study the reactions of metal anodes with electrolyte and the formation of SEIs [44,45].…”

Section: Current and Future Challengesmentioning

confidence: 99%

2023 Roadmap on molecular modelling of electrochemical energy materials

Zhang,

Cheng,

Chen

et al. 2023

J. Phys. Energy

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New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as empowered by machine learning techniques can help us to understand, control and design electrochemical energy materials at atomistic precision. Therefore, this roadmap, which is a collection of authoritative opinions, serves as a gateway for both the experts and the beginners to have a quick overview of the current status and corresponding challenges in molecular modelling of electrochemical energy materials for batteries, supercapacitors, CO2RR, and fuel cell applications.

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