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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Santiago Rigamonti, Maria Troppenz, Martin Kubáň, Claudia Draxl et al.
8 citations
npj Computational Materials
Machine Learning in Materials Science
0.82
article

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

Santiago Rigamonti, Maria Troppenz, Martin Kubáň, Claudia Draxl, Axel Hübner
article en
8 citations

Abstract

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.

npj Computational MaterialsVol. 10(1)
Humboldt-Universität zu Berlin (DE)
European Commission, Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 26%
Machine Learning in Materials Science
0.82
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

CELL: a Python package for cluster expansion with a focus on complex alloys — Santiago Rigamonti, Maria Troppenz, et al. · npj Computational Materials (2024) | TGRS Research Map | TGRS