Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability

@article{Ryltsev2021DeepML,
  title={Deep machine learning potentials for multicomponent metallic melts: development, predictability and compositional transferability},
  author={Roman Ryltsev and N M Chtchelkatchev},
  journal={Journal of Molecular Liquids},
  year={2021}
}
The use of machine learning interatomic potentials (MLIPs) in simulations of materials is a state-of-the-art approach, which allows achieving nearly \textit{ab initio} accuracy with orders of magnitude less computational cost. Multicomponent disordered systems have a highly complicated potential energy surface due to both topological and compositional disorder. That arises issues in MLIPs developing, such as optimal design strategy of potentials and their predictability and transferability… 

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