Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915499537858560 |
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| author | Kundu, Sohang Chamaki, Diana Ye, Hong-Zhou Agarwal, Garvit Berkelbach, Timothy C. |
| author_facet | Kundu, Sohang Chamaki, Diana Ye, Hong-Zhou Agarwal, Garvit Berkelbach, Timothy C. |
| contents | We study the ring-opening decomposition of ethylene carbonate in the presence of a single lithium atom and on the surface of lithium metal. Combining accurate electronic structure theory, enhanced sampling, and machine learning, we fine-tune the MACE-MP0 foundation model and apply the resulting machine learning potentials to obtain statistically converged free energy profiles and reaction rates. We confirm that the level of electronic structure theory is important, and inaccurate density functionals can overestimate the reaction rate by up to nine orders of magnitude. We also find that harmonic transition state theory underestimates reaction rates by about one order of magnitude. For the surface reaction, we find and characterize a new, ultrafast decomposition pathway wherein the carbonyl is deeply inserted into the lithium surface and bent by about 70$^\circ$. This reaction, which occurs in a few tens of picoseconds, generates a ring-opened intermediate that is a precursor for CO or CO$_2$ formation; by contrast, an alternative pathway that yields CO$_3^{2-}$ and ethylene is found to be non-competitive, occurring on a timescale of tens of nanoseconds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14067 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials Kundu, Sohang Chamaki, Diana Ye, Hong-Zhou Agarwal, Garvit Berkelbach, Timothy C. Chemical Physics Materials Science We study the ring-opening decomposition of ethylene carbonate in the presence of a single lithium atom and on the surface of lithium metal. Combining accurate electronic structure theory, enhanced sampling, and machine learning, we fine-tune the MACE-MP0 foundation model and apply the resulting machine learning potentials to obtain statistically converged free energy profiles and reaction rates. We confirm that the level of electronic structure theory is important, and inaccurate density functionals can overestimate the reaction rate by up to nine orders of magnitude. We also find that harmonic transition state theory underestimates reaction rates by about one order of magnitude. For the surface reaction, we find and characterize a new, ultrafast decomposition pathway wherein the carbonyl is deeply inserted into the lithium surface and bent by about 70$^\circ$. This reaction, which occurs in a few tens of picoseconds, generates a ring-opened intermediate that is a precursor for CO or CO$_2$ formation; by contrast, an alternative pathway that yields CO$_3^{2-}$ and ethylene is found to be non-competitive, occurring on a timescale of tens of nanoseconds. |
| title | Reaction dynamics of lithium-mediated electrolyte decomposition using machine learning potentials |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2509.14067 |