Abstract
Over the past few years, graphynes (GYs), one of the latest families of carbon allotropes, have been investigated as promising materials for various applications, including lithium-ion batteries. Developing analytic potentials for describing potential energy surfaces underlying cation-π interactions is a challenging task for large-scale simulations involving carbon-based materials. This study aimed to develop effective analytic models to overcome computational challenges in accurately capturing cation-π interactions between alkali metal ions (Li+, Na+, K+) and GYs, which are crucial for ion storage and transport within nanoporous GY frameworks. The high computational demands of ab initio methods for such large systems make analytic modeling increasingly essential. The absence of reliable analytic potentials and parameters needs to be urgently addressed. Here, we report that the commonly used 12-6 Lennard-Jones (LJ) potential combined with electrostatics (EL) is insufficient for representing cation-π interactions, as it fails to capture the ion-induced dipole effects that are crucial for the problem in hand. To address this, we propose modified 12-4 LJ + EL and improved Lennard-Jones (ILJ) potentials, which better capture the necessary short-range repulsions and long-range attractions. We developed and validated parameter sets based on interaction energy curves from density functional theory (DFT), enabling a reasonably accurate modeling of cation permeation and surface dynamics on GY sheets. This work not only advances the understanding of cation-π interactions in GYs but also introduces computationally efficient models that support the design of GYs for next-generation energy storage applications.
Supplementary materials
Title
Cation-π Interactions Involving Graphynes: An Intermolecular Force Field Formulation Featuring Ion-Induced Dipole Effects
Description
Initial and optimized parameters of analytic potentials, potential energy profiles showing the influence of peripheral hydrogen atoms, SAPT0 results, schematic representations, and optimized geometries.
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