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Constructing multicomponent cluster expansions with machine-learning and chemical embedding.

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Summary
This summary is machine-generated.

We introduce the embedded cluster expansion (eCE) formalism for modeling multicomponent alloys. This method accurately predicts alloy properties without increasing model complexity, enabling studies of complex materials.

Keywords:
Atomistic modelsComputational methodsMetals and alloys

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Area of Science:

  • Computational Materials Science
  • Alloy Theory
  • Statistical Mechanics

Background:

  • Cluster expansions are vital surrogate models linking electronic structure to alloy properties.
  • Modeling multicomponent alloys with traditional cluster expansions faces challenges due to increased parameters and dataset size.

Purpose of the Study:

  • To introduce the embedded cluster expansion (eCE) formalism for accurate on-lattice surrogate models of multicomponent alloys.
  • To demonstrate eCE's ability to handle alloys with several chemical species efficiently.

Main Methods:

  • The eCE model learns a low-dimensional embedding of site basis functions and energy model weights simultaneously.
  • A senary alloy from groups 5 and 6 was used to test the eCE formalism.

Main Results:

  • eCE models accurately reproduce ordering energetics in complex alloys without significant complexity increase.
  • eCE models effectively extrapolate into new compositional spaces by leveraging elemental similarities.

Conclusions:

  • The embedded cluster expansion formalism enables accurate surrogate modeling for multicomponent alloys.
  • eCE opens new avenues for studying complex alloys with numerous chemical elements.