Machine-learning interatomic potential for radiation damage and defects in tungsten

@article{Byggmstar2019MachinelearningIP,
  title={Machine-learning interatomic potential for radiation damage and defects in tungsten},
  author={Jesper Byggm{\"a}star and A. Hamedani and Kai Nordlund and Flyura Djurabekova},
  journal={Physical Review B},
  year={2019}
}
We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self… 
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References

SHOWING 1-10 OF 176 REFERENCES
Interatomic potentials for modelling radiation defects and dislocations in tungsten.
TLDR
The trends predicted for the Peierls barrier of the [Formula: see text] screw dislocation are in qualitative agreement with ab initio calculations, enabling quantitative comparison of the predicted kink-pair formation energies with experimental data.
An empirical potential for simulating vacancy clusters in tungsten.
TLDR
An empirical interatomic potential for tungsten is presented, particularly well suited for simulations of vacancy-type defects, and it is shown that the new potential predicts low-energy defect structures and formation energies with high accuracy.
Machine Learning a General-Purpose Interatomic Potential for Silicon
The success of first principles electronic structure calculation for predictive modeling in chemistry, solid state physics, and materials science is constrained by the limitations on simulated length
Accuracy and transferability of Gaussian approximation potential models for tungsten
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian approximation potential framework, fitted to a database of first-principles density
Deep learning inter-atomic potential model for accurate irradiation damage simulations
We propose a hybrid scheme that interpolates smoothly the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential with a newly developed deep learning potential energy model. The
Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron
We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The
Radiation damage in tungsten from cascade overlap with voids and vacancy clusters.
TLDR
A systematic molecular dynamics investigation of the effects of overlap of collision cascades in tungsten with pre-existing vacancy- type defects finds that overlap of a cascade with a vacancy-type defect decreases the number of new defects with roughly the same functional dependence as previously shown for interstitial clusters.
...
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