Predicting Electrolyte Redox Potential Trends through Long-Range Electrostatics and Configuration Ensembles: Insights...

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• Electrolyte stability is crucial for advanced energy storage, particularly for lithium- and sodium-ion batteries.
• Redox potentials determine the electrochemical window, but accurately computing them, especially long-range electrostatics, is challenging.
• Existing solvation schemes have limitations in electrolyte system applications.

Purpose of the Study:

• To develop and validate an integrated computational approach for estimating electrolyte electrochemical stability.
• To accurately predict redox potentials and electrochemical windows of electrolyte materials.
• To investigate the impact of solvation shells and configurational ensembles on computed properties.

Main Methods:

• Combined classical molecular dynamics (MD) simulations and quantum mechanics-molecular mechanics (QM/MM) calculations.
• MD simulations generated representative solution configurations for ensemble averaging.
• QM/MM calculations determined frontier orbital energies (HOMO, LUMO) and vertical ionization potentials (IP) and electron affinities (EA).

Main Results:

• Including multiple solvation shells and averaging over configuration ensembles is essential for accurate frontier orbital computation.
• The integrated approach successfully computed oxidative and reductive stability trends for common battery electrolytes.
• Computed results showed good agreement with experimental linear sweep voltammetry data.

Conclusions:

• Accurate prediction of electrolyte electrochemical stability necessitates accounting for long-range electrostatic effects and configurational ensembles.
• Structural relaxation of oxidized/reduced species significantly impacts redox potential predictions, challenging simple HOMO-LUMO correlations.
• The developed QM/MM framework provides a robust method for evaluating electrolyte stability in energy storage applications.