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

Abstract : This paper introduces the KineCluE code that implements the self-consistent mean-field theory for clusters offinite size. The transport coefficients of a system are then obtained as a sum over cluster contributions (clusterexpansion formalism), each being individually obtained with KineCluE. This method allows to go beyond theinfinitely 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 single cluster, all kinetic trajectories areaccounted for in an exact manner. The code, written in Python, adapts to a wide variety of systems, withvarious crystallographic systems (eventually strained), defects and solute types and number, and various jumpmechanisms, including collective ones. The code also features interesting tools such as the sensitivity studyroutine which allows to identify the most important jump frequencies to get accurate transport coefficients.
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https://hal-cea.archives-ouvertes.fr/cea-02339613
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Submitted on : Monday, November 4, 2019 - 1:19:59 PM
Last modification on : Wednesday, June 24, 2020 - 4:18:35 PM

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T. Schuler, L. Messina, M. Nastar. KineCluE a Kinetic Cluster Expansion code to compute transport coefficients beyond the dilute limit. Computational Materials Science, Elsevier, 2020, 172, pp.109191. ⟨cea-02339613⟩

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