An electrostatic spectral neighbor analysis potential 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 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.
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
- Sponsoring Org.:
- USDOE Office of Science (SC); US Department of the Navy, Office of Naval Research (ONR); National Science Foundation (NSF)
- OSTI Identifier:
- 1559268
- Grant/Contract Number:
- AC02-05CH11231
- Resource Type:
- Accepted Manuscript
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 42 ENGINEERING
Citation Formats
Deng, Zhi, Chen, Chi, Li, Xiang-Guo, and Ong, Shyue Ping. An electrostatic spectral neighbor analysis potential for lithium nitride. United States: N. p., 2019.
Web. doi:10.1038/s41524-019-0212-1.
Deng, Zhi, Chen, Chi, Li, Xiang-Guo, & Ong, Shyue Ping. An electrostatic spectral neighbor analysis potential for lithium nitride. United States. https://doi.org/10.1038/s41524-019-0212-1
Deng, Zhi, Chen, Chi, Li, Xiang-Guo, and Ong, Shyue Ping. Tue .
"An electrostatic spectral neighbor analysis potential for lithium nitride". United States. https://doi.org/10.1038/s41524-019-0212-1. https://www.osti.gov/servlets/purl/1559268.
@article{osti_1559268,
title = {An electrostatic spectral neighbor analysis potential for lithium nitride},
author = {Deng, Zhi and Chen, Chi and Li, Xiang-Guo and Ong, Shyue Ping},
abstractNote = {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.},
doi = {10.1038/s41524-019-0212-1},
journal = {npj Computational Materials},
number = 1,
volume = 5,
place = {United States},
year = {Tue Jul 16 00:00:00 EDT 2019},
month = {Tue Jul 16 00:00:00 EDT 2019}
}
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