Composition-Dependent Li-Ion Transport in Mixed-Metal ZIF-62 Glasses Revealed by Machine-Learning Molecular Dynamics
1Department of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar 382355, Gujarat, India.
ACS Applied Materials & Interfaces
|April 20, 2026
Summary
We developed a machine learning model to study lithium-ion transport in metal-organic frameworks. Intermediate metal substitution in ZIF-62 optimizes ion conductivity, paving the way for better solid-state batteries.
Area of Science:
- Materials Science
- Chemistry
- Physics
Background:
- Understanding ion transport in metal-organic frameworks (MOFs) is crucial for developing advanced energy storage materials.
- Key factors influencing ion mobility include framework dynamics, local disorder, and hopping mechanisms over various timescales.
- Metal-organic frameworks like ZIF-62 offer tunable structures for potential applications in solid-state electrolytes.
Purpose of the Study:
- To investigate lithium-ion (Li-ion) transport in pure and mixed-metal ZIF-62 using advanced computational methods.
- To elucidate the relationship between framework composition, dynamics, and ionic conductivity.
- To demonstrate the utility of deep neural network interatomic potentials for studying ion transport in MOFs.
Main Methods:
- Development and application of a deep neural network (DNN) interatomic potential trained with density functional theory (DFT) data via the DP-GEN framework.
- Execution of large-scale deep potential molecular dynamics (MD) simulations at 450 K.
- Utilizing analytical techniques including Van Hove correlation and jump-resolved analysis.
Main Results:
- Li-ion motion is characterized by localized vibrations within cages and activated hopping through the pore network.
- Intermediate metal substitution (Co/(Co + Zn) ≈ 0.25-0.50) enhances Li-ion displacement while maintaining structural stability.
- The 0.50-ZIF-62 composition shows the lowest activation energy (~0.093 eV) and highest ionic conductivity (~6.0 × 10-3 S cm-1).
- Long-range diffusion is primarily driven by independent hopping events.
Conclusions:
- A dynamic and unified picture of Li-ion transport in mixed-metal MOF electrolytes has been established.
- Machine-learning potentials accurately predict ion transport properties, enabling rational material design.
- Optimized mixed-metal ZIF-62 compositions hold promise for high-conductivity solid-state ion conductors.
Keywords:
Deep neural network potentialsIonic conductivityLithium-ion transportMOF glassesMachine-learning molecular dynamicsMetal−organic frameworks (MOFs)More Related Videos
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