Speeding up the development of solid state electrolyte by machine learning
Qianyu Hu,
Kunfeng Chen,
Jinyu Li,
Tingting Zhao,
Feng Liang,
Dongfeng Xue
Affiliations
Qianyu Hu
Institute of Novel Semiconductors, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China
Kunfeng Chen
Institute of Novel Semiconductors, State Key Laboratory of Crystal Materials, Shandong University, Jinan 250100, China; Corresponding authors.
Jinyu Li
College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China
Tingting Zhao
College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China; Corresponding authors.
Feng Liang
Key Laboratory for Nonferrous Vacuum Metallurgy of Yunnan Province, National Engineering Research Center of Vacuum Metallurgy, Faculty of Metallurgical and Energy Engineering Kunming University of Science and Technology, Kunming 650093, China; Corresponding authors.
Dongfeng Xue
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China; Corresponding authors.
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.
