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Bridging the Gap: Using Machine Learning Force Fields to Simulate Gold Break Junctions at Pulling Speeds Closer to

William Bro-Jørgensen1, Joseph M Hamill1, Davide Donadio2

  • 1Department of Chemistry and Nano-Science Center, University of Copenhagen, Copenhagen Ø DK-2100, Denmark.

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|November 13, 2025
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Summary

Machine-learning force fields reveal complex behaviors in gold nanowire junctions, unlike classical models. These advanced simulations bridge experimental and simulation time scales for accurate molecular junction studies.

Keywords:
Seebeck coefficientbreak junctionsgold nanowiresmachine learning force fieldsmolecular electronicssingle-molecule junctionsthermopower

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Area of Science:

  • Materials Science
  • Computational Physics
  • Nanotechnology

Background:

  • Gold nanowires are crucial for studying molecular junctions' electronic and thermal properties.
  • Simulating realistic gold nanowire junctions is challenging due to time scale discrepancies and classical force field inaccuracies.

Purpose of the Study:

  • To investigate phenomena in gold-gold pulling junctions using machine-learning force fields.
  • To address the limitations of classical force fields in simulating metallic nanowire behavior.

Main Methods:

  • Utilized machine-learning force fields for atomistic simulations of gold-gold pulling junctions.
  • Compared simulation results with classical force field models.

Main Results:

  • Machine-learning force fields captured phenomena missed by classical force fields.
  • Discovered a dependence of average breaking distance on pulling speed in gold nanowires.
  • Revealed more complex structural evolution than previously understood.

Conclusions:

  • Accurate force fields, particularly machine-learning based ones, are essential for simulating metallic nanowires.
  • Advanced modeling bridges the gap between experimental and simulation time scales for molecular junctions.