Diffusion in liquid metals is directed by competing collective modes

The self-diffusion process in a dense liquid is influenced by collective particle movements. Extensive molecular dynamics simulations for liquid aluminium and rubidium evidence a crossover in the diffusion coefficient at about 1.4 times the melting temperature <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:msub><a:mi>T</a:mi><a:mi>m</a:mi></a:msub></a:math>, indicating a profound change in the diffusion mechanism. The corresponding velocity autocorrelation functions demonstrate a decrease of the cage effect with a gradual set in of a power-law decay, they celebrate long time tail. This behavior is caused by a competition of density fluctuations near the melting point with vortex-type particle patterns from transverse currents in the hot fluid. The investigation of the velocity autocorrelation function evidences a gradual transition in dynamics with rising temperature. The competition between these two collective particle movements, one hindering and one enhancing the diffusion process, leads to a non-Arrhenius-type behavior of the diffusion coefficient around <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:mrow><b:mn>1.4</b:mn><b:mspace width="0.16em"/><b:msub><b:mi>T</b:mi><b:mi>m</b:mi></b:msub></b:mrow></b:math>, which signals the transition from a dense to a fluidlike liquid dynamics in the potential energy landscape picture.

Authors

Institutions

Publication Details

Journal
Physical review. B./Physical review. B
Published
2025-02-06
DOI
https://doi.org/10.1103/physrevb.111.l081104
Citations
5
Primary Topic
nanoparticles nucleation surface interactions
Type
article
Field-Weighted Citation Impact
6.50
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Diffusion in liquid metals is directed by competing collective modes

F. Demmel, N. Jakse
5 citations
Physical review. B./Physical review. B
nanoparticles nucleation surface interactions
6.50
article

Diffusion in liquid metals is directed by competing collective modes

F. Demmel, N. Jakse
article en
5 citations

Abstract

The self-diffusion process in a dense liquid is influenced by collective particle movements. Extensive molecular dynamics simulations for liquid aluminium and rubidium evidence a crossover in the diffusion coefficient at about 1.4 times the melting temperature <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:msub><a:mi>T</a:mi><a:mi>m</a:mi></a:msub></a:math>, indicating a profound change in the diffusion mechanism. The corresponding velocity autocorrelation functions demonstrate a decrease of the cage effect with a gradual set in of a power-law decay, they celebrate long time tail. This behavior is caused by a competition of density fluctuations near the melting point with vortex-type particle patterns from transverse currents in the hot fluid. The investigation of the velocity autocorrelation function evidences a gradual transition in dynamics with rising temperature. The competition between these two collective particle movements, one hindering and one enhancing the diffusion process, leads to a non-Arrhenius-type behavior of the diffusion coefficient around <b:math xmlns:b="http://www.w3.org/1998/Math/MathML"><b:mrow><b:mn>1.4</b:mn><b:mspace width="0.16em"/><b:msub><b:mi>T</b:mi><b:mi>m</b:mi></b:msub></b:mrow></b:math>, which signals the transition from a dense to a fluidlike liquid dynamics in the potential energy landscape picture.

Physical review. B./Physical review. BVol. 111(8)
Institut polytechnique de Grenoble (FR), Rutherford Appleton Laboratory (GB), Université Grenoble Alpes (FR)
Openalex Percentile: Top 4%
nanoparticles nucleation surface interactions
6.50
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.