Superionic Ionic Conductor Discovery via Multiscale Topological Learning.

Area of Science:

• Materials Science
• Computational Chemistry
• Energy Storage

Background:

• Lithium superionic conductors (LSICs) are essential for next-generation solid-state batteries, providing high ionic conductivity and safety.
• Discovering new LSICs is hindered by vast chemical spaces, limited data, and complex structure-property relationships for ion transport.
• Optimizing ion transport in LSICs requires a deep understanding of their intricate structural and chemical properties.

Purpose of the Study:

• To introduce a novel multiscale topological learning (MTL) framework for efficient LSIC discovery.
• To overcome the challenges of vast chemical spaces and limited data in identifying promising LSIC candidates.
• To develop a scalable tool for accelerating the discovery of materials with superior ion transport properties.

Main Methods:

• Integrated algebraic topology and unsupervised learning to model substructures and extract multiscale topological features.
• Introduced topological screening metrics (cycle density, minimum connectivity distance) to ensure structural integrity and ion diffusion pathways.
• Employed unsupervised clustering for candidate identification and ab initio molecular dynamics for final validation.

Main Results:

• The MTL framework successfully identified 14 novel lithium superionic conductor candidates.
• Four of the newly discovered LSICs have been independently validated through experimental testing.
• The developed topological screening metrics effectively ensured structural connectivity and ion diffusion compatibility.

Conclusions:

• The multiscale topological learning framework significantly accelerates the discovery of novel LSICs.
• This approach offers a scalable and adaptable solution for complex materials discovery challenges.
• The validated LSICs hold promise for advancing solid-state battery technology for renewable energy and electric vehicles.