Moment tensor potentials as a promising tool to study diffusion processes

@article{Novoselov2019MomentTP,
  title={Moment tensor potentials as a promising tool to study diffusion processes},
  author={I.I. Novoselov and Alexey Yanilkin and Alexander V. Shapeev and E. V. Podryabinkin Dukhov Research Institute of Automatics and Moscow Institute of Physics and Technology and Skolkovo Institute of Science},
  journal={Computational Materials Science},
  year={2019}
}
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References

SHOWING 1-10 OF 49 REFERENCES
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
TLDR
This paper considers the problem of constructing interatomic potentials that approximate a given quantum-mechanical interaction model, and proposes a new class of systematically improvable potentials which are proposed, analyzed, and tested on an existing quantum- mechanical database.
Active learning of linearly parametrized interatomic potentials
Ab initio based empirical potential used to study the mechanical properties of molybdenum
Density-functional theory energies, forces, and elastic constants determine the parametrization of an empirical, modified embedded-atom method potential for molybdenum. The accuracy and…
Quantum molecular dynamics: Accelerating diffusion via parallel replica method.
First-principles calculation of self-diffusion coefficients.
TLDR
A first-principles method is demonstrated to compute all factors entering the vacancy-mediated self-diffusion coefficient and yields a quantitative description of the migration energy and vibrational prefactor via transition state theory.
A ternary EAM interatomic potential for U–Mo alloys with xenon
A new interatomic potential for a uranium–molybdenum system with xenon is developed in the framework of an embedded atom model using a force-matching technique and a dataset of ab initio atomic…
Quantifying the anomalous self-diffusion in molybdenum with first-principles simulations
First-principles molecular-dynamics simulations based on a recently developed exchange-correlation functional show that self-diffusion in the refractory metal molybdenum is associated with strongly…
First-principles calculations of self-diffusion constants in silicon.
TLDR
The first parameter-free calculations of self-diffusion constants in silicon are reported, with the self-interstitial mechanism dominating over the contribution of the other mechanisms.
Generalized Gradient Approximation Made Simple.
TLDR
A simple derivation of a simple GGA is presented, in which all parameters (other than those in LSD) are fundamental constants, and only general features of the detailed construction underlying the Perdew-Wang 1991 (PW91) GGA are invoked.
Self-diffusion rates in Al from combined first-principles and model-potential calculations.
TLDR
Monovacancy diffusion alone dominates over diffusion due to divacancies and interstitials in Al for all temperatures up to the melting point, without fitting to experimental data.
...
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