CELL: a Python package for cluster expansion with a focus on complex alloys
Abstract We present the Python package , which provides a modular approach to the cluster expansion (CE) method. 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 . 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/ ). 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 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.
Authors
- Santiago Rigamonti (ORCID: https://orcid.org/0000-0002-8939-6173)
- Maria Troppenz (ORCID: https://orcid.org/0000-0001-8817-2330)
- Martin Kubáň (ORCID: https://orcid.org/0000-0002-1619-2460)
- Claudia Draxl (ORCID: https://orcid.org/0000-0003-3523-6657)
- Axel Hübner (ORCID: https://orcid.org/0000-0002-4159-5712)
Institutions
- Humboldt-Universität zu Berlin (DE)
Publication Details
- Journal
- npj Computational Materials
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1038/s41524-024-01363-x
- Citations
- 8
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.82
Funders
- European Commission
- Deutsche Forschungsgemeinschaft