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

Bro-Jørgensen, William; Hamill, Joseph; Donadio, Davide; Solomon, Gemma C.

doi:10.1021/acsnano.5c11887

Article (Scientific journals)

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

Bro-Jørgensen, William; Hamill, Joseph; Donadio, Davide et al.

2025 • In ACS Nano, 19 (46), p. 39735–39746

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/345701

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10.1021/acsnano.5c11887

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41231750

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Abstract :

[en] he properties and dynamics of gold nanowires have been studied for decades as an important testbed for several physical phenomena. Gold nanowires forming at contacts are an integral part of molecular junctions used to study the electronic and thermal properties of single molecules. However, the huge discrepancy in time scales between experiments and simulations, compounded by the limited accuracy of classical force fields, has posed a challenge in accurately simulating realistic junctions. Here, we show that machine-learning force fields uncover phenomena not captured by classical force fields when modeling Au−Au pulling junctions. Our simulations show a dependency of the average breaking distance on the pulling speed, highlighting a more complex behavior than previously thought. Our results demonstrate that the use of more accurate force fields to simulate metallic nanowires is essential for capturing the complexity of their structural evolution in break junction experiments. Our developments advance the modeling accuracy of molecular junctions, bridging the gap between experimental and simulation time scales.

Disciplines :

Chemistry
Physics

Author, co-author :

Bro-Jørgensen, William

Hamill, Joseph ; Université de Liège - ULiège > Molecular Systems (MolSys)

Donadio, Davide

Solomon, Gemma C.

Language :

English

Title :

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

Publication date :

November 2025

Journal title :

ACS Nano

ISSN :

1936-0851

eISSN :

1936-086X

Publisher :

American Chemical Society (ACS)

Volume :

Issue :

Pages :

39735–39746

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 03 June 2026

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