Abstract
Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li3N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb–Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants, and phonon dispersion curves. We also demonstrate the application of eSNAP in long-time, large-scale Li diffusion studies in Li3N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large-scale atomistic simulations for such systems.
Highlights
A potential energy surface (PES) that yields potential energy of a system of atoms with given atomic coordinates is the fundamental enabler for atomistic simulation methods
For Li3N, we calculated the electrostatic energy by assigning formal charges 1 and −3 to Li and N, respectively
We find that the phonon dispersion curves calculated from electrostatic spectral neighbor analysis potential (SNAP) (eSNAP) are in good agreement with that from density functional theory (DFT)
Summary
A potential energy surface (PES) that yields potential energy of a system of atoms with given atomic coordinates is the fundamental enabler for atomistic simulation methods. Ab initio or first principles methods that solve the Schrödinger equation, typically some approximation within the Kohn-Sham density functional theory (DFT) framework,[1,2] can be applied to directly calculate the PES. While such methods are highly accurate and transferable across diverse chemistries and bonding types, their high computational cost limit their application in molecular dynamics (MD) simulations to relatively small and simple systems containing up to a few hundreds of atoms and sub-nanosecond time scales. The functional form of these potentials are drastically simplified with only a few fitting parameters to satisfy physical considerations.[3,4] the accuracy of the empirical potentials is necessarily limited by the approximations made in selecting the functional form, which are generally not transferable to another system with different bonding types
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