Leveraging transfer learning for accurate estimation of ionic migration barriers in solids.

ABSTRACT: Rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors, is exponentially dependent on the ionic migration barrier (E _m ) within solids, a difficult-to-estimate quantity. Previous approaches to identify materials with low E _m have often relied on imprecise descriptors or rules-of-thumb. Here, we present a graph-neural-network-based architecture that leverages principles of transfer learning to efficiently and accurately predict E _m across a variety of materials. We use a model (labeled MPT) that has been simultaneously pre-trained on seven bulk properties, introduce architectural modifications to build inductive bias on different migration pathways in a structure, and sub

SUBMITTER: Devi R 

PROVIDER: S-EPMC12904795 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature