Rapid <em>in-silico</em> Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Electrolytes are important components in lithium-ion batteries. However, battery degradation due to irreversible electrochemical reactions in the electrolyte can consume electrolyte molecules and severely reduce its effective operation lifetime. It is hence important to study the electrochemical reaction pathways in the battery electrolyte to further improve lithium-ion battery reliability. Unfortunately, it is difficult to experimentally study the exact electrolyte electrochemical reaction pathways in the electrolyte, which usually contains many chemical species (different solvents, salts, additive molecules, and electrode interfaces). These reaction byproducts may form complex solid electrolyte interphase (SEI) on the electrolyte-electrode interface (LiF, Li2CO3, Li2O, LiOH, organolithium compounds, etc), which are more difficult to analyze, instead of being released as the easier-to-analyze small gas molecules. Our recent publication on the tiered tensor transform technique in combination with density functional theory software (tiered tensor transform- Vienna Ab-initio Simulation Package (VASP), or 3T-VASP) enables the practical usage of the ab-initio approach to generate physically meaningful electrolyte electrochemical reaction byproducts within just 100-150 DFT steps. In this work, we describe the inner workings of the 3T-VASP code (publicly available on Github) in greater detail, along with the simulation preparation steps needed to correctly set up the 3T-VASP workflow for new electrolyte systems of interest beyond the examples presented in the original 3T-VASP publication.

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Publication Details

Journal
Journal of Visualized Experiments
Published
2025-08-22
DOI
https://doi.org/10.3791/68854
Primary Topic
Fuel Cells and Related Materials
Type
article
Field-Weighted Citation Impact
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article

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Jonathan P. Mailoa, J. Ren J. Ren, Zhengmi Tang, Min Ni et al.
Journal of Visualized Experiments
Fuel Cells and Related Materials
article

Rapid in-silico Battery Electrolyte Electrochemical Reaction Generation using 3T-VASP Multi-Scale Energy Minimization

Jonathan P. Mailoa, J. Ren J. Ren, Zhengmi Tang, Min Ni, Shengyu Zhang, Xin Li
article en

Abstract

Electrolytes are important components in lithium-ion batteries. However, battery degradation due to irreversible electrochemical reactions in the electrolyte can consume electrolyte molecules and severely reduce its effective operation lifetime. It is hence important to study the electrochemical reaction pathways in the battery electrolyte to further improve lithium-ion battery reliability. Unfortunately, it is difficult to experimentally study the exact electrolyte electrochemical reaction pathways in the electrolyte, which usually contains many chemical species (different solvents, salts, additive molecules, and electrode interfaces). These reaction byproducts may form complex solid electrolyte interphase (SEI) on the electrolyte-electrode interface (LiF, Li2CO3, Li2O, LiOH, organolithium compounds, etc), which are more difficult to analyze, instead of being released as the easier-to-analyze small gas molecules. Our recent publication on the tiered tensor transform technique in combination with density functional theory software (tiered tensor transform- Vienna Ab-initio Simulation Package (VASP), or 3T-VASP) enables the practical usage of the ab-initio approach to generate physically meaningful electrolyte electrochemical reaction byproducts within just 100-150 DFT steps. In this work, we describe the inner workings of the 3T-VASP code (publicly available on Github) in greater detail, along with the simulation preparation steps needed to correctly set up the 3T-VASP workflow for new electrolyte systems of interest beyond the examples presented in the original 3T-VASP publication.

Journal of Visualized Experiments(222)
Tencent (China) (CN)
Affordable and clean energy
Openalex Percentile: Top 80%
Fuel Cells and Related Materials
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