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Journal of Materials Research

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Machine learning, Self-optimizing systems


While machine-learned interatomic potentials have become a mainstay for modeling materials, designing training sets that lead to robust potentials is challenging. Automated methods, such as active learning and on-the-fly learning, construct reliable training sets, but these processes can be resource-intensive. Current training approaches often use density functional theory (DFT) calculations that have the same cell size as the simulations that the potential is explicitly trained to model. Here, we demonstrate an easy-to-implement small-cell training protocol and use it to model the Zr-H system. This training leads to a potential that accurately predicts known stable Zr-H phases and reproduces the α-β pure zirconium phase transition in molecular dynamics simulations. Compared to traditional active learning, small-cell training decreased the training time of the α-β zirconium phase transition by approximately 20 times. The potential describes the phase transition with a degree of accuracy similar to that of the large-cell training method.


© Copyright the author(s) 2024



This is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published as: iere, J. A., Luo, Y., Xia, Y., Béland, L. K., Daymond, M. R., & Hart, G. L. (2023). Accelerating training of MLIPs through small-cell training. Journal of Materials Research, 38(24), 5095-5105.



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