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Spatial Separation of Molecular Conformers and Clusters
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Block sparsity promoting algorithm for efficient construction of cluster expansion models for multicomponent alloys.

Krishnamohan Thekkepat1,2,3, Sumanjit Das4, Debi Prosad Dogra4

  • 1Indo-Korea Science and Technology Center, Jakkur, Bangalore 560065, India.

Journal of Physics. Condensed Matter : an Institute of Physics Journal
|September 2, 2023
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A new algorithm efficiently constructs cluster expansion (CE) models for multicomponent alloys. This method significantly reduces training data and speeds up configuration sampling, enabling faster materials discovery.

Keywords:
Monte Carlo simulationalloyscluster expansiondisordered materials

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Alloy Theory

Background:

  • Multicomponent alloys are crucial for advanced structural and energy storage applications.
  • The complexity of multicomponent alloys hinders traditional first-principles computational modeling.
  • The cluster expansion (CE) method, using machine learning, is a key tool for modeling alloy disorder.

Purpose of the Study:

  • To present a novel compressive sensing-based algorithm for efficient CE Hamiltonian construction in multicomponent alloys.
  • To develop a method for creating sparse, physically meaningful CE models from minimal training data.
  • To accelerate the computational modeling of multicomponent alloy thermodynamics.

Main Methods:

  • Developed a compressive sensing algorithm for constructing sparse CE Hamiltonians.
  • Utilized a small, carefully selected training set of alloy structures.
  • Applied the algorithm to four diverse alloy systems: Ag-Au, Ag-Au-Cu, Ag-Au-Cu-Pd, and (Ge,Sn)(S,Se,Te).

Main Results:

  • Achieved over 50% reduction in training set size compared to conventional methods.
  • Generated sparse CE models that enable at least 3x faster configuration space sampling.
  • Successfully reproduced known ground state orderings and order-disorder transitions for the tested alloys.

Conclusions:

  • The developed algorithm significantly reduces the cost and time for constructing CE models for multicomponent alloys.
  • This method facilitates high-throughput computational screening of multicomponent alloys.
  • Enables more efficient exploration of alloy phase diagrams and material properties.