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Tungsten is a promising candidate material in fusion energy facilities.Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, sothey are crucial for the understanding of the macroscopic propertydeterioration of tungsten under harsh and complex service environments. Theinteratomic potential used in the MD simulations is required to accuratelydescribe a wide spectrum of relevant defect properties, which is by farchallenging to the existing interatomic potentials. In this paper, we propose anew three-body embedding descriptor and hybridize it into the Deep-Potential(DP) framework, an end-to-end deep learning interatomic potential model.Trained with the dataset generated by a concurrent learning method, thepotential model for tungsten, named by DP-HYB, is able to accurately predict awide range of properties including elastic constants, stacking fault energy,the formation energies of free surfaces and point defects, which are includedin the training dataset, and formation energies of grain boundaries andprismatic loops, the core structure of screw dislocation, the Peierls barrierand the transition path of the screw dislocation migration, which are notexplicitly included in the training dataset. The DP-HYB is a good candidate forthe atomistic simulations of tungsten property deterioration, especially thoseinvolving the mechanical property degradation under the harsh fusion serviceenvironment.

A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment