Active Learning of Diffusion Pathways for Machine-Learned Interatomic Potentials*

In chemically complex systems such as MPEAs, the number of elemental permutations involved in the local environment of even simple diffusion pathways, such as vacancy and interstitial, grows factorially with the number of alloying elements. As such, it becomes computationally intractable to employ ab initio methods to study diffusion kinetics with representative size-scale structures or through pathway enumeration. Here we show that machine-learned interatomic potentials (MLIPs) offer a compelling solution since they can scale to representative sizes and can be explicitly trained on diffusion pathways to reproduce them accurately. To that end, we present a workflow that includes different diffusion pathway sampling strategies used in conjunction with active learning to generate MLIP training data for kinetic modelling. We report on best practices for data-efficient training using as our benchmark system, NbTiZr alloys with oxygen interstitials, which recently have shown to exhibit complex passivation dynamics.