Revealing Electronic Structure-Chemisorption Relationships for Accelerated Discovery of Aqueous Zinc Battery...

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Aqueous Zn-ion batteries (AZIBs) face challenges with zinc anode rechargeability.
• Electrolyte additives are crucial for improving AZIB performance, with adsorption energy on the zinc surface being a key predictor.

Purpose of the Study:

• To develop a machine learning (ML) framework for predicting the adsorption energy of organic electrolyte additives on zinc anodes.
• To enable rapid and accurate screening of additives for enhanced AZIB cycle life.

Main Methods:

• Utilized Density Functional Theory (DFT) to calculate electronic features of 301 organic additives.
• Employed statistical moments of sp-band density of states and frontier molecular orbitals (HOMO/LUMO) as ML model inputs.
• Evaluated seven ML models, with Random Forest showing the best performance.

Main Results:

• The Random Forest model achieved high accuracy (RMSE: 0.140 eV, MAE: 0.113 eV) in predicting adsorption energies.
• Highlighted the importance of the sp-band shape (third-order moments) and LUMO energy in determining adsorption trends.
• Established a correlation between electronic structure and chemical reactivity, analogous to the d-band model for transition metals.

Conclusions:

• The developed ML framework offers an efficient method for high-throughput screening of electrolyte additives for AZIBs.
• Uncovered fundamental relationships between organic molecule electronic structure and their interaction with metal surfaces.
• Provides insights for designing superior electrolyte additives to improve AZIB stability and efficiency.