Effect of the mechanical strength on the ion transport in a transition metal lithium halide electrolyte: first-principle calculations

Ren, Yuan; Sun, Changjie; Liu, Jingjing; Cai, Guojian; Tan, Xin; Zhang, Chao

doi:10.1016/j.mtcomm.2022.104570

Cited by 10 publications

(4 citation statements)

References 23 publications

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“…The remaining 8 superionic SSE, including Li 3 ErCl 6 , are predominantly situated within the range of positions 110 to 190. Six of the nine superionic SSEs verified through AIMD simulations have already been studied as superionic SSEs. ,,– Except for these, three candidates have not yet been studied as SSE, and their ionic conductivity has not been calculated: LiBiS 2 (semiconductor), RbLi 6 B 2 O 6 F (not studied), and BaLi(BS 2 ) 3 (optical materials). , In this respect, we propose these three superionic Li-SSE as promising candidates, and further experiments are recommended to confirm their functionality as a component in the battery cell.…”

Section: Resultsmentioning

confidence: 99%

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Kang,

Kim,

Min

2023

J. Phys. Chem. C

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Li-ion solid-state electrolytes (Li-SSEs) hold promise to solve critical issues related to conventional Li-ion batteries (LIBs), such as the flammability of liquid electrolytes and dendrite growth. In this study, we develop a platform involving a high-throughput screening process and machine learning surrogate model for identifying superionic Li-SSEs among 19,480 Li-containing materials. Li-SSE candidates are selected based on the screening criteria, and their ionic conductivities are predicted. For the training database, the ionic conductivities and crystal systems of various inorganic SSEs, such as Na SuperIonic CONductor (NASICON), argyrodite, and halide, are obtained from previous literature. Subsequently, a chemical descriptor (CD), crystal system, and number of atoms are used as machine-readable features. To reduce the uncertainty in the surrogate model, the ensemble method, which considers the two best-performing models, is employed; the mean prediction accuracies are found to be 0.887 and 0.886, respectively. Furthermore, first-principles calculations are conducted to confirm the ionic conductivities of the strong candidates. Finally, three potential superionic Li-SSEs that have not been previously investigated are proposed. We believe that the platform constructed and explored in this work can accelerate the search for Li-SSEs with satisfactory performance at a minimum cost.

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Section: Resultsmentioning

confidence: 99%

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Kang,

Kim,

Min

2023

J. Phys. Chem. C

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“…The calculated migration barrier energies of different pathways are relatively high, which may be attributed to the softBV software itself. [ 41,49,50 ]…”

Section: Resultsmentioning

confidence: 99%

Encapsulating and Operating a Stable Li₃ErBr₆‐Based Solid Li–SeS₂ Battery at Room Temperature

Shi

Zeng

Zhang

et al. 2023

Adv Funct Materials

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Rare earth‐based halide solid electrolytes (RE‐HSEs) are noted due to their comprehensive properties of good ionic conductivity, electrochemical window, and deformability. However, the types and applications of RE‐HSEs need to be further expanded. Here, a highly purified RE‐HSE Li3ErBr6 (LErB) is synthesized successfully by a vacuum evaporation method. The prepared LErB possesses a high ionic conductivity of 1.0 mS cm−1 with an electrochemical window of 1.5–3.4 V. The LErB is applied in a solid Li–SeS2 battery, which can realize stable cycling performance for 300 cycles and good rate performance at room temperature. Moreover, to evaluate the practicability of the LErB‐based Li–SeS2 battery, a pouch‐type cell is assembled, which can light a diode array even after bending and cutting. These results demonstrate that LErB‐based solid Li–SeS2 cells possess advantages in battery packaging and operation, which can improve the practicability of all‐solid‐state lithium batteries.

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“…The electron and ion relaxation convergence precision values are 1.0 × 10 −4 eV and 1.0 × 10 −3 eV, respectively, while the force convergence criterion is 0.02 eV Å −1 . 50 …”

Section: Methodsmentioning

confidence: 99%

Fe-modified NASICON-type Na₃V₂(PO₄)₃ as a cathode material for sodium ion batteries

Liu,

Xu,

Ren

et al. 2024

RSC Adv.

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Effect of the mechanical strength on the ion transport in a transition metal lithium halide electrolyte: first-principle calculations

Cited by 10 publications

References 23 publications

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Encapsulating and Operating a Stable Li₃ErBr₆‐Based Solid Li–SeS₂ Battery at Room Temperature

Fe-modified NASICON-type Na₃V₂(PO₄)₃ as a cathode material for sodium ion batteries

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Effect of the mechanical strength on the ion transport in a transition metal lithium halide electrolyte: first-principle calculations

Cited by 10 publications

References 23 publications

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Discovery of Superionic Solid-State Electrolyte for Li-Ion Batteries via Machine Learning

Encapsulating and Operating a Stable Li3ErBr6‐Based Solid Li–SeS2 Battery at Room Temperature

Fe-modified NASICON-type Na3V2(PO4)3 as a cathode material for sodium ion batteries

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Product

Resources

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Encapsulating and Operating a Stable Li₃ErBr₆‐Based Solid Li–SeS₂ Battery at Room Temperature

Fe-modified NASICON-type Na₃V₂(PO₄)₃ as a cathode material for sodium ion batteries