Login / Signup

Machine-learning prediction of the formation of atomic gold wires by mechanically controlled break junctions.

Aishwaryo GhoshBiswajit PabiAtindra Nath PalTanusri Saha-Dasgupta
Published in: Nanoscale (2023)
One of the challenging issues in the formation of atomic wires in break-junction experiments is the formation of stable monoatomic chains of reasonable length. To address this issue, in this study, we present a combination of unsupervised and supervised machine learning models trained on the experimentally measured conductance traces, with a goal to develop a microscopic understanding. Applying a machine learning model to two independent data sets from two different samples containing 72 000 and 90 000 conductance-displacement traces of single-atomic junctions, respectively, we first obtain the optimum conditions of bias and the stretching rate for the formation of chains of length > 4 Å. A deep learning method is subsequently applied for the classification of individual breaking traces, leading to the identification of trace features related to long-chain formation. Further investigation by ab initio molecular dynamics simulations provides a molecular-level understanding of the problem.
Keyphrases
  • machine learning
  • deep learning
  • molecular dynamics simulations
  • artificial intelligence
  • big data
  • single molecule
  • molecular docking
  • body composition
  • resistance training
  • drug induced
  • electron microscopy