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A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten

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Abstract

So far, it has been a challenge for existing interatomic potentials to accurately describe a wide range of physical properties and maintain reasonable efficiency. In this work, we develop an interatomic potential for simulating radiation damage in body-centered cubic tungsten by employing deep potential, a neural network-based deep learning model for representing the potential energy surface. The resulting potential predicts a variety of physical properties consistent with first-principles calculations, including phonon spectrum, thermal expansion, generalized stacking fault energies, energetics of free surfaces, point defects, vacancy clusters, and prismatic dislocation loops. Specifically, we investigated the elasticity-related properties of prismatic dislocation loops, i.e., their dipole tensors, relaxation volumes, and elastic interaction energies. This potential is found to predict the maximal elastic interaction energy between two 1/2 \(\left\langle {1 \, 1 \, 1} \right\rangle\) loops better than previous potentials, with a relative error of only 7.6%. The predicted threshold displacement energies are in reasonable agreement with experimental results, with an average of 128 eV. The efficiency of the present potential is also comparable to the tabulated gaussian approximation potentials and modified embedded atom method potentials, meanwhile, can be further accelerated by graphical processing units. Extensive benchmark tests indicate that this potential has a relatively good balance between accuracy, transferability, and efficiency.

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Data availability

The data that support the findings of this study are openly available in Gitee repository at https://gitee.com/DingChangjie/tungsten-dp.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (Grant No. 2022YFE03110000), the National Natural Science Foundation of China (Nos. 52171084 and 12192282), and the Foundation of President of Hefei Institutes of Physical Science, Chinese Academy of Sciences (Nos. YZJJQY202203 and BJPY2021A05). Computations were performed on Hefei Advanced Computing Center and the Center for Computational Science, Hefei Institutes of Physical Science. We sincerely thank Dr. Hao Wang at Peking University for helpful suggestions on the implementation of tabulation, and Prof. Takuji Oda at Seoul National University for the kind help of obtaining the interatomic potentials.

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  1. Key Laboratory of Materials Physics, Institute of Solid State Physics, HFIPS, Chinese Academy of Sciences, Hefei, 230031, China

    Chang-Jie Ding, Ya-Wei Lei, Xiao-Lin Li, Xiang-Yan Li, Yan-Ge Zhang, Yi-Chun Xu, Chang-Song Liu & Xue-Bang Wu

  2. Science Island, University of Science and Technology of China, Hefei, 230026, China

    Chang-Jie Ding & Xiao-Lin Li

  3. Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing, 100088, China

    Xiao-Yang Wang

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Ding, CJ., Lei, YW., Wang, XY. et al. A deep learning interatomic potential suitable for simulating radiation damage in bulk tungsten. Tungsten 6, 304–322 (2024). https://doi.org/10.1007/s42864-023-00230-4

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