| Abstrakt: |
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 111loops 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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