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

TL;DR

CELL is a versatile Python package that enables advanced cluster expansion modeling for complex alloys, supporting multi-component, multi-sublattice systems with robust data handling, visualization, and interfacing capabilities.

Contribution

It introduces a modular, user-friendly Python toolkit for cluster expansion applicable to complex, multi-component alloys with various lattice structures.

Findings

Successfully modeled Cu-Pt surface alloy with oxygen adsorption.

Analyzed order-disorder transition and thermodynamics.

Demonstrated applicability to complex clathrate compounds.

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.