Franck Rothen | Searcharxiv

arXiv2025

Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale

The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization of polarization vectors is of substantial importance to ferroelectrics research. Here, we develop a fully automated three-dimensional piezoresponse force microscopy (Auto-3DPFM) technique automating all essential steps in interferometric PFM for 3D polarization vector characterization, including laser alignment, tip calibration and approach, image acquisition, polarization vector reconstruction, and visualization. The automation reduces the experimental burden of ferroelectric polarization vector characterization, while the back-and-forth calibration ensures consistency and reproducibility of 3D polarization reconstruction. An algorithmic workflow is also developed to identify domain walls and calculate their characteristic angles via a spatial vector-angle-difference method, presenting one unique capability enabled by Auto-3DPFM that is not accessible with traditional PFM techniques. Beyond representing a significant step forward in 3D polarization mapping, Auto-3DPFM promises to accelerate discovery via high-throughput and autonomous characterization in ferroelectric materials research. When integrated with machine learning and adaptive sampling strategies in self-driving labs, Auto-3DPFM will serve as a valuable tool for advancing ferroelectric physics and microelectronics development.

cond-mat.mtrl-sci↗

arXiv2024

Enhancing generalization in high energy physics using white-box adversarial attacks

Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations from Monte Carlo simulations, potentially limiting their ability to generalize well to real data. This study aims to enhance the generalization properties of supervised models by reducing the sharpness of local minima. It reviews the application of four distinct white-box adversarial attacks in the context of classifying Higgs boson decay signals. The attacks are divided into weight-space attacks and feature-space attacks. To study and quantify the sharpness of different local minima, this paper presents two analysis methods: gradient ascent and reduced Hessian eigenvalue analysis. The results show that white-box adversarial attacks significantly improve generalization performance, albeit with increased computational complexity.

hep-ph↗