Speeding up the development of solid state electrolyte by machine learning

Autor: Qianyu Hu, Kunfeng Chen, Jinyu Li, Tingting Zhao, Feng Liang, Dongfeng Xue
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Next Energy, Vol 5, Iss , Pp 100159- (2024)
Druh dokumentu: article
ISSN: 2949-821X
DOI: 10.1016/j.nxener.2024.100159
Popis: Solid-state electrolytes have been demonstrated immense potential with their high density and safety for Li, Na batteries. The discovery of novel crystals is of fundamental scientific and technological interest in solid-state chemistry. The discovery, synthesis and application of energetically favourable solid-state electrolytes has been bottlenecked by expensive trial-and-error approaches. Machine learning has brought breakthroughs to solid-state electrolytes. Numerous solid-state electrolyte candidates have been screened by different models at multiscale, i.e., interatomic potentials, molecular dynamics, ionic conductivity. Machine learning method also accelerate the synthesis prediction, mechanism discovery and interface design. This review would answer the question what can be done for solid-state electrolytes by machine learning, including descriptor, model, algorithm etc. This paper will promote fast integration between scientists in materials, software, computing discipline.
Databáze: Directory of Open Access Journals