Jose L. Martinez | Searcharxiv

arXiv2025

Enhanced Ferromagnetism in Monolayer Cr2Te3 via Topological Insulator Coupling

Exchange-coupled interfaces are pivotal in exploiting two-dimensional (2D) ferromagnetism. Due to the extraordinary correlations among charge, spin, orbital and lattice degrees of freedom, layered magnetic transition metal chalcogenides (TMCs) bode well for exotic topological phenomena. Here we report the realization of wafer-scale Cr2Te3 down to monolayer (ML) on insulating SrTiO3(111) and/or Al2O3(001) substrates using molecular beam epitaxy. Robust ferromagnetism persists in the 2D limit. In particular, the Curie temperature TC of 2 ML Cr2Te3 increases from 100 K to ~ 120 K when proximitized to topological insulator (TI) (Bi,Sb)2Te3, with substantially boosted magnetization as observed via polarized neutron reflectometry. Our experiments and theory strongly indicate that the Bloembergen-Rowland interaction is likely universal underlying TC enhancement in TI-coupled magnetic heterostructures. The topological-surface-enhanced magnetism in 2D TMC enables further exchange coupling physics and quantum hybrid studies, including paving the way to realize interface-modulated topological electronics.

cond-mat.mtrl-sci↗

arXiv2025

Leveraging Prior Mean Models for Faster Bayesian Optimization of Particle Accelerators

Tuning particle accelerators is a challenging and time-consuming task that can be automated and carried out efficiently using suitable optimization algorithms, such as model-based Bayesian optimization techniques. One of the major advantages of Bayesian algorithms is the ability to incorporate prior information about beam physics and historical behavior into the model used to make control decisions. In this work, we examine incorporating prior accelerator physics information into Bayesian optimization algorithms by utilizing fast executing, neural network models trained on simulated or historical datasets as prior mean functions in Gaussian process models. We show that in ideal cases, this technique substantially increases convergence speed to optimal solutions in high-dimensional tuning parameter spaces. Additionally, we demonstrate that even in non-ideal cases, where prior models of beam dynamics do not exactly match experimental conditions, the use of this technique can still enhance convergence speed. Finally, we demonstrate how these methods can be used to improve optimization in practical applications, such as transferring information gained from beam dynamics simulations to online control of the LCLS injector, and transferring knowledge gained from experimental measurements across different operating modes, such as accelerating different ion species at the ATLAS heavy ion accelerator.

physics.acc-ph↗