General-purpose machine-learned potential for 16 elemental metals and their alloys
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2024-12
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en
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15
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Nature Communications, Volume 15, issue 1, pp. 1-15
Abstract
Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach’s effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys.Description
Publisher Copyright: © The Author(s) 2024.
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Song, K, Zhao, R, Liu, J, Wang, Y, Lindgren, E, Wang, Y, Chen, S, Xu, K, Liang, T, Ying, P, Xu, N, Zhao, Z, Shi, J, Wang, J, Lyu, S, Zeng, Z, Liang, S, Dong, H, Sun, L, Chen, Y, Zhang, Z, Guo, W, Qian, P, Sun, J, Erhart, P, Ala-Nissila, T, Su, Y & Fan, Z 2024, ' General-purpose machine-learned potential for 16 elemental metals and their alloys ', Nature Communications, vol. 15, no. 1, 10208, pp. 1-15 . https://doi.org/10.1038/s41467-024-54554-x