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• W2926222015 abstract "A selective sampling procedure is applied to reduce the number of density functional theory calculations needed to find energetically favorable grain boundary structures. The procedure is based on a machine learning algorithm involving a Gaussian process, and uses statistical modelling to map the energies of the all grain boundaries. Using the procedure, energetically favorable grain boundaries in BaZrO3 are identified with up to 85% lower computational cost than the brute force alternative of calculating all possible structures. Furthermore, our results suggest that using a grid size of 0.3 Å in each dimension is sufficient when creating grain boundary structures using such sampling procedures." @default.
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• W2926222015 date "2019-06-01" @default.
• W2926222015 modified "2023-10-12" @default.
• W2926222015 title "Application of machine learning-based selective sampling to determine BaZrO3 grain boundary structures" @default.
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• W2926222015 doi "https://doi.org/10.1016/j.commatsci.2019.03.054" @default.
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