Connecting Scales: Learning Dynamics for Efficient Ionic Conductivity Predictions with Graphs

Volha Turchyna, Artem Maevskiy, Alexandra Carvalho, Andrey E Ustyuzhanin

Track: Short paper

Keywords: Solid Electrolytes, Ionic Conductivity, Accelerated Material Discovery, Equivariant Graph Neural Networks

TL;DR: This study explores the use of graph neural networks to link micro-scale interactions with macro-scale ionic conductivity, highlighting its potential utility for solid electrolyte discovery.

Abstract: Multiscale approaches are crucial for advancing our understanding of material properties, particularly in the search for novel solid electrolytes essential for solid-state batteries. Estimating ionic conductivity through traditional molecular dynamics (MD) simulations is computationally intensive, requiring significant time to capture macro-scale behavior from micro-scale interatomic interactions. This work addresses the challenge of connecting micro-scale interatomic potentials with macro-scale conductivity measurements. We propose using equivariant graph neural networks to develop a faster mapping between these scales, significantly enhancing the efficiency of ionic diffusion predictions. This proof-of-concept demonstrates the potential to accelerate material discovery for solid electrolytes, addressing a critical need in energy storage technology.

Presenter: ~Volha_Turchyna1

Submission Number: 35