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An electrostatic spectral neighbor analysis potential for lithium nitride
2019
npj Computational Materials
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-Li 3 N, a prototypical lithium superionic conductor of interest as a solid
doi:10.1038/s41524-019-0212-1
fatcat:jjogqehtrbgbbpftgk6xetdlpi