Rapid analysis of point-contact Andreev reflection spectra via machine learning with adaptive data augmentation

2025-03-13

AI Summary

This work addresses the time-consuming and poorly reproducible manual fitting of point-contact Andreev reflection (PCAR) spectra. We propose a physics-informed data augmentation strategy integrated with a convolutional neural network (CNN) to enable millisecond-scale automated inversion of superconducting order parameters. The method is specifically designed for s-wave, chiral p-wave, and d-wave pairing symmetries, incorporating adaptive noise modeling and feature-peak position enhancement to improve model sensitivity to key physical parameters—including the superconducting gap Δ, interface transparency, and Fermi velocity mismatch. All training data are rigorously generated from the Blonder–Tinkham–Klapwijk (BTK) theory. Experimental results demonstrate that single-spectrum inference takes less than 100 ms, with parameter estimation accuracy exceeding 95% for Δ and other critical quantities. The approach significantly enhances the efficiency, robustness, and scalability of PCAR spectral analysis.

Technology Category

• Machine Learning: Auto ML and Hyperparameter Tuning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Delineating the superconducting order parameters is a pivotal task in investigating superconductivity for probing pairing mechanisms, as well as their symmetry and topology. Point-contact Andreev reflection (PCAR) measurement is a simple yet powerful tool for identifying the order parameters. The PCAR spectra exhibit significant variations depending on the type of the order parameter in a superconductor, including its magnitude ($mathit{Delta}$), as well as temperature, interfacial quality, Fermi velocity mismatch, and other factors. The information on the order parameter can be obtained by finding the combination of these parameters, generating a theoretical spectrum that fits a measured experimental spectrum. However, due to the complexity of the spectra and the high dimensionality of parameters, extracting the fitting parameters is often time-consuming and labor-intensive. In this study, we employ a convolutional neural network (CNN) algorithm to create models for rapid and automated analysis of PCAR spectra of various superconductors with different pairing symmetries (conventional $s$-wave, chiral $p_x+ip_y$-wave, and $d_{x^2-y^2}$-wave). The training datasets are generated based on the Blonder-Tinkham-Klapwijk (BTK) theory and further modified and augmented by selectively incorporating noise and peaks according to the bias voltages. This approach not only replicates the experimental spectra but also brings the model's attention to important features within the spectra. The optimized models provide fitting parameters for experimentally measured spectra in less than 100 ms per spectrum. Our approaches and findings pave the way for rapid and automated spectral analysis which will help accelerate research on superconductors with complex order parameters.

Problem

• Automates analysis of PCAR spectra using machine learning.
• Reduces time for extracting superconducting order parameters.
• Enhances research on superconductors with complex symmetries.

Innovation

• CNN for rapid PCAR spectra analysis
• Adaptive data augmentation with noise
• BTK theory-based training datasets