An Electrostatic Spectral Neighbor Analysis Potential (eSNAP) for Lithium Nitride

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 $\alpha$-\ce{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 \ce{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.

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Cited by in corpus (1)

• A Performance and Cost Assessment of Machine Learning Interatomic Potentials