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Luca Messina

Researcher at Royal Institute of Technology

Publications -  36
Citations -  631

Luca Messina is an academic researcher from Royal Institute of Technology. The author has contributed to research in topics: Vacancy defect & Kinetic Monte Carlo. The author has an hindex of 13, co-authored 31 publications receiving 447 citations. Previous affiliations of Luca Messina include University of Lugano & Université Paris-Saclay.

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Exact ab initio transport coefficients in bcc Fe − X ( X = Cr , Cu , Mn , Ni , P , Si ) dilute alloys

TL;DR: In this article, flux coupling phenomena such as solute drag by vacancies and radiation-induced segregation at defect sinks are systematically investigated for six bcc iron-based dilute binary alloys, containing Cr, Cu, Mn, Ni, P and Si impurities, respectively.
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Systematic electronic-structure investigation of substitutional impurity diffusion and flux coupling in bcc iron

TL;DR: In this paper, the diffusion properties of a wide range of impurities (transition metals and Al, Si, and P) in ferritic alloys are investigated by means of a combined ab initio-atomic diffusion theory approach.
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Solute diffusion by self-interstitial defects and radiation-induced segregation in ferritic Fe–X (X=Cr, Cu, Mn, Ni, P, Si) dilute alloys

TL;DR: In this article, the authors investigated solute transport due to self-interstitial defects and radiation induced segregation tendencies in dilute ferritic alloys, by computing the transport coefficients of each system based on ab initio calculations of binding energies, migration rates, as well as formation and migration vibrational entropies.
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KineCluE: A kinetic cluster expansion code to compute transport coefficients beyond the dilute limit

TL;DR: The KineCluE code is introduced that implements the self-consistent mean-field theory for clusters of finite size, and 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.