Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key?
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| Format: | Preprint |
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2025
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| _version_ | 1866918247438221312 |
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| author | Zhong, Peichen Persson, Kristin A. |
| author_facet | Zhong, Peichen Persson, Kristin A. |
| contents | Despite decades of study, the identity of the dominant \ce{Li+}-conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/\ce{Li2O}/\ce{Li2CO3} nanocrystallites within a disordered matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interfaces, grain boundaries, and amorphous phases may instead host the primary fast-ion pathways. Using diffusion-based generative structure prediction and machine-learning interatomic potentials (MLIPs), we investigate lithium difluorophosphate (\ce{LiPO2F2}), a key mixed-anion decomposition product of phosphorus- and fluorine-containing electrolytes. We identify a stable crystalline polymorph and demonstrate that the amorphous counterpart is conductive, with projected room-temperature $σ\approx 0.18$ mS cm$^{-1}$ and $E_\mathrm{a} \approx 0.40$ eV. This enhancement stems from structural disorder flattening the Li site-energy landscape and a low formation energy for Li-interstitial defects, which supplies additional mobile carriers. We propose amorphous mixed-anion Li--P--O--F phases as a promising conducting medium in the SEI, offering a specific target for engineering improved battery interfaces. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_22912 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key? Zhong, Peichen Persson, Kristin A. Materials Science Computational Physics Despite decades of study, the identity of the dominant \ce{Li+}-conducting phase within the inorganic SEI of Li-ion batteries remains unresolved. While the mosaic model describes LiF/\ce{Li2O}/\ce{Li2CO3} nanocrystallites within a disordered matrix, these crystalline phases inherently offer limited ionic conductivity. Growing evidence suggests that interfaces, grain boundaries, and amorphous phases may instead host the primary fast-ion pathways. Using diffusion-based generative structure prediction and machine-learning interatomic potentials (MLIPs), we investigate lithium difluorophosphate (\ce{LiPO2F2}), a key mixed-anion decomposition product of phosphorus- and fluorine-containing electrolytes. We identify a stable crystalline polymorph and demonstrate that the amorphous counterpart is conductive, with projected room-temperature $σ\approx 0.18$ mS cm$^{-1}$ and $E_\mathrm{a} \approx 0.40$ eV. This enhancement stems from structural disorder flattening the Li site-energy landscape and a low formation energy for Li-interstitial defects, which supplies additional mobile carriers. We propose amorphous mixed-anion Li--P--O--F phases as a promising conducting medium in the SEI, offering a specific target for engineering improved battery interfaces. |
| title | Machine-Learning-Guided Insights into Solid-Electrolyte Interphase Conductivity: Are Amorphous Lithium Fluorophosphates the Key? |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2510.22912 |