Machine-learning-assisted discovery of lattice dynamics signatures of sodium superionic conductors

Abstract

Sodium superionic conductors are key to the development of all-solid-state sodium batteries. Discovery of new superionic conductors has traditionally relied on insights from material defect chemistry and the transition/hopping theory, while the role of lattice vibrations, i.e., phonons, remains underexplored. We identify key lattice dynamics signatures that govern ionic conductivity by analyzing the phonon mean squared displacement (MSD) of Na⁺ ions. By high-throughput screening of a dataset of 3903 Na-containing structures, we establish a strong positive correlation between phonon MSD and diffusion coefficients, providing a quantitative correlation between lattice dynamics and ion transport. To accelerate this discovery, we incorporate machine learning (ML) into our screening workflow, using phonon-derived descriptors to rapidly predict ionic transport properties across a broad structural space. Our findings reveal that low acoustic cutoff phonon frequencies, low center vibrational density of states of Na⁺ ions, slightly higher than the acoustic cutoff frequencies, and enhanced low-frequency vibrational coupling between Na⁺ ions and the host sublattice promote superionic conductivity. Phonon mode analysis further demonstrates that only a small subset of low-frequency acoustic and optic modes contribute dominantly to large phonon MSDs and Na⁺ ion migration, while higher-energy modes contribute negligibly. These insights enable the integration of lattice dynamics descriptors, phonon MSD, Na⁺ VDOS center, acoustic cutoff frequency, and low-frequency phonon coupling into machine learning frameworks, accelerating the discovery and rational design of high-performance sodium superionic conductors.