KineCluE: a Kinetic Cluster Expansion code to compute transport coefficients beyond the dilute limit

TL;DR

KineCluE is a Python code that calculates transport coefficients for clusters beyond dilute conditions using a cluster expansion approach, accounting for all kinetic trajectories exactly and adaptable to various systems.

Contribution

The paper presents KineCluE, a novel Python tool implementing a self-consistent mean-field theory for finite clusters to compute transport properties beyond dilute limits.

Findings

Calculates transport coefficients beyond dilute limit

Accounts for all kinetic trajectories exactly

Flexible for various materials and mechanisms

Abstract

This paper introduces the KineCluE code that implements the self-consistent mean-field theory for clusters of finite size. Transport coefficients are obtained as a sum over cluster contributions (in a cluster expansion formalism), each being individually computed with KineCluE. This method allows for the calculation of these coefficients beyond the infinitely dilute limit, and is an important step in bridging the gap between dilute and concentrated approaches. Inside a finite volume of space containing the components of a given cluster, all kinetic trajectories are accounted for in an exact manner. The code, written in Python, adapts to a wide variety of systems, with various crystallographic structures (possibly under strain), defects and solute amount and types, and various jump mechanisms, including collective ones. The code also features a set of useful tools, such as the sensitivity study routine that allows for the identification of the most important jump frequencies to get accurate transport coefficients with minimum computational cost.