Machine-learned interatomic potentials for alloys and alloy phase diagrams

@article{Rosenbrock2021MachinelearnedIP,
  title={Machine-learned interatomic potentials for alloys and alloy phase diagrams},
  author={Conrad W. Rosenbrock and Konstantin Gubaev and Alexander V. Shapeev and L{\'i}via B. P{\'a}rtay and Noam Bernstein and G{\'a}bor Cs{\'a}nyi and Gus L. W. Hart},
  journal={npj Computational Materials},
  year={2021},
  volume={7},
  pages={1-9}
}
We introduce machine-learned potentials for Ag-Pd to describe the energy of alloy configurations over a wide range of compositions. We compare two different approaches. Moment tensor potentials (MTPs) are polynomial-like functions of interatomic distances and angles. The Gaussian approximation potential (GAP) framework uses kernel regression, and we use the smooth overlap of atomic position (SOAP) representation of atomic neighborhoods that consist of a complete set of rotational and… 
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