AI Day 2026
Description
• Motivation: Accelerating the Discovery of Superionic Conductors
→ The development of all-solid-state batteries relies on the availability of fast lithium and sodium ion conductors. However, identifying new superionic materials remains a major challenge due to the vast chemical space and the high computational cost of conventional approaches such as density functional theory (DFT) and ab initio molecular dynamics (AIMD).
• Challenge: Lack of Efficient Predictive Descriptors
→ While ionic transport is governed by atomic-scale interactions, there is a lack of simple, physically interpretable descriptors that can reliably predict fast ion conduction without performing expensive simulations. As a result, large-scale screening of materials remains inefficient.
Key Idea: Lattice Dynamics Governs Ion Transport
→ In this work, we use machine learning interatomic potential combine with lattice dynamics calculations to identify key vibrational descriptor such as phonon MSD, Debye frequency, acoustic cutoff, and VDOS band center, for rapid discovery of Li and Na superionic conductors.
Publication Info
2026.
© 2026, Ogheneyoma Aghoghovbia, Mohammed Al-Fahdi, & Ming Hu