Unlocking Lithium Superionic Conduction via Phonon Softness Descriptors: A High-Throughput Machine Learning Paradigm
Abstract
Lithium superionic conductors (LISICONs) are pivotal for next-generation solid-state batteries, yet rational design remains challenged by poorly understood correlations between lattice dynamics (phonons) and ion transport. We unveil phonon softness as the unifying descriptor for rapid Li + conduction through a high-throughput computational framework combining ab initio calculations, machine learning potentials (fine-tuned MACE MPA model), and molecular dynamics across 1304 dynamically stable Li materials screened from an initial pool of 8578 candidates. We demonstrate that low-frequency phonons dominate Li + migration, with the Li + vibrational density of states (VDOS) center and Debye frequency exhibiting strong inverse correlations with diffusion coefficients. Phonon mode-resolved analysis reveals that delocalized, collective vibrations (
Fields of Study & Subjects
Advanced Battery Materials and Technologies
Machine Learning in Materials Science
Thermal Expansion and Ionic Conductivity
Publication details
- Journal / source
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Volume
- 38
- Issue
- 4
- Pages
- 1689–1705
- Publication date
- 10 February 2026
- Language
- EN