Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

Abstract

Developing electrolytes with excellent electrochemical stability is critical for next-generation rechargeable batteries. Super-concentrated electrolytes (SCEs) have attracted great interest due to their high electrochemical performances and stability. Previous studies have revealed changes in solvation structures and shifts in lowest unoccupied molecular orbitals from solvents to anions, promoting the formation of an anion-derived solid-electrolyte-interphase (SEI) in SCE. However, a direct connection at the atomic level to electrochemical properties is still missing, hindering the rational optimization of electrolytes. Herein, we combine ab initio molecular dynamics with the free energy calculation method to compute redox potentials of propylene carbonate electrolytes at a range of LiTFSI concentrations, and moreover employ an unsupervised machine learning model with a local structure descriptor to establish the structure–property relations. Our calculation indicates that the network of TFSI in SCE not only helps stabilize the added electron and renders the anion more prone to reductive decomposition, but also impedes the solvation of F and favors LiF precipitation, together leading to effective formation of protective SEI layers. Our work provides new insights into the solvation structures and electrochemistry of concentrated electrolytes which are essential to electrolyte design in batteries.

Graphical abstract: Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

Supplementary files

Article information

Article type
Edge Article
Submitted
19 Jul 2022
Accepted
07 Sep 2022
First published
15 Sep 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2022, Advance Article

Unraveling the origin of reductive stability of super-concentrated electrolytes from first principles and unsupervised machine learning

F. Wang and J. Cheng, Chem. Sci., 2022, Advance Article , DOI: 10.1039/D2SC04025E

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