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Leveraging transfer learning for accurate estimation of ionic migration barriers in solids
Reshma Devi, Keith T. Butler, Gopalakrishnan Sai Gautam
https://arxiv.org/abs/2508.06436

Leveraging transfer learning for accurate estimation of ionic migration barriers in solids
Ionic mobility determines the rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors and is exponentially dependent on the migration barrier ($E_m$), a difficult to measure/calculate quantity. Previous approaches to identify materials with high ionic mobility have relied on imprecise descriptors given the lack of generalizable models to predict $E_m$. Here, we present a graph neural network based architecture that leverages principles of transfer le…