Machine learning delta-T noise for temperature bias estimation DOI
Matthew Gerry, Jonathan J. Wang, Joanna Li

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(8)

Published: Feb. 26, 2025

Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It characterized by a quadratic dependence on temperature difference and nonlinear relationship with transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble atomic-scale can be utilized estimate bias these systems. Our approach employs supervised machine learning algorithm train neural network, input features being scaled electrical conductance, mean temperature. Due limited experimental data, generate synthetic datasets, designed mimic experiments. The trained was subsequently applied predict biases from datasets. Using performance metrics, bias—the deviation predicted differences their true value—is less than 1 K for junctions conductance up 4G0. study highlights that, while single measurement insufficient accurately estimating due contributions other sources, averaging over enables predictions within uncertainties. This suggests approaches estimation similarly stimuli junctions.

Language: Английский

Machine learning delta-T noise for temperature bias estimation DOI
Matthew Gerry, Jonathan J. Wang, Joanna Li

et al.

The Journal of Chemical Physics, Journal Year: 2025, Volume and Issue: 162(8)

Published: Feb. 26, 2025

Delta-T shot noise is activated in temperature-biased electronic junctions, down to the atomic scale. It characterized by a quadratic dependence on temperature difference and nonlinear relationship with transmission coefficients of partially opened conduction channels. In this work, we demonstrate that delta-T noise, measured across an ensemble atomic-scale can be utilized estimate bias these systems. Our approach employs supervised machine learning algorithm train neural network, input features being scaled electrical conductance, mean temperature. Due limited experimental data, generate synthetic datasets, designed mimic experiments. The trained was subsequently applied predict biases from datasets. Using performance metrics, bias—the deviation predicted differences their true value—is less than 1 K for junctions conductance up 4G0. study highlights that, while single measurement insufficient accurately estimating due contributions other sources, averaging over enables predictions within uncertainties. This suggests approaches estimation similarly stimuli junctions.

Language: Английский

Citations

0