CELL: a Python package for cluster expansion with a focus on complex alloys

We present the Python package CELL , which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their parent lattice . CELL uses state-of-the-art techniques for the construction of training data sets, model selection, and finite-temperature simulations. The user interface consists of well-documented Python classes and modules ( http://sol.physik.hu-berlin.de/cell/ ). CELL also provides visualization utilities and can be interfaced with virtually any ab initio package, total-energy codes based on interatomic potentials, and more. The usage and capabilities of CELL are illustrated by a number of examples, comprising a Cu-Pt surface alloy with oxygen adsorption, featuring two coupled binary sublattices, and the thermodynamic analysis of its order-disorder transition; the demixing transition and lattice-constant bowing of the Si-Ge alloy; and an iterative CE approach for a complex clathrate compound with a parent lattice consisting of 54 atoms.

Publication Details

Journal
edoc Publication server (Humboldt University of Berlin)
Published
2024-08-30
DOI
https://doi.org/10.18452/31956
Primary Topic
Machine Learning in Materials Science
Type
article
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article

CELL: a Python package for cluster expansion with a focus on complex alloys

edoc Publication server (Humboldt University of Berlin)
Machine Learning in Materials Science
article

CELL: a Python package for cluster expansion with a focus on complex alloys

article en

Abstract

We present the Python package CELL , which provides a modular approach to the cluster expansion (CE) method. CELL can treat a wide variety of substitutional systems, including one-, two-, and three-dimensional alloys, in a general multi-component and multi-sublattice framework. It is capable of dealing with complex materials comprising several atoms in their parent lattice . CELL uses state-of-the-art techniques for the construction of training data sets, model selection, and finite-temperature simulations. The user interface consists of well-documented Python classes and modules ( http://sol.physik.hu-berlin.de/cell/ ). CELL also provides visualization utilities and can be interfaced with virtually any ab initio package, total-energy codes based on interatomic potentials, and more. The usage and capabilities of CELL are illustrated by a number of examples, comprising a Cu-Pt surface alloy with oxygen adsorption, featuring two coupled binary sublattices, and the thermodynamic analysis of its order-disorder transition; the demixing transition and lattice-constant bowing of the Si-Ge alloy; and an iterative CE approach for a complex clathrate compound with a parent lattice consisting of 54 atoms.

edoc Publication server (Humboldt University of Berlin)
Openalex Percentile: Top 71%
Machine Learning in Materials Science
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CELL: a Python package for cluster expansion with a focus on complex alloys · edoc Publication server (Humboldt University of Berlin) (2024) | TGRS Research Map | TGRS