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Multi-principal element alloys (MPEAs) are exciting systems showing remarkable properties compared to conventional materials due to their exceedingly large compositional space and spatially varying chemical environment. However, predicting fundamental properties from the local chemical environment is challenging due to the large scale of the problem. To investigate this fundamental problem, we employ a combination of atomistic simulations (using ab-initio and molecular dynamics) and convolutional neural networks (CNNs) to evaluate point defect and migration energies in an equimolar CoFeCrNi MPEA. We show how energies of point defects can be predicted with reasonable accuracy using a small subset of local chemical environments. Using the CNNs, we develop a lattice Monte Carlo simulation that computes the migration path and diffusivities of vacancies. Remarkably, our work illustrates how the local chemical environment leads rise to a distribution function of the point defect energies, which is responsible for the macroscopic diffusivity of vacancies. In particular, we observed that vacancies get trapped in super basins surrounded by large migration and connected with low migration energy states. As a result, vacancy diffusivity is highly dependent on the environment and could change several orders of magnitude for a given temperature. Our works illustrate the importance of understanding properties in MPEAs depending on the local chemical environment and the ability of CNN to provide a model for computing energies in high-dimensional spaces, which can be used to scale things up to higher-order models. |
