Publications & Preprints

Resolving the instantaneous, position-dependent isotropic magnetic exchange coupling Jij across a dynamically deforming crystal lattice requires a computational approach that simultaneously handles structural forces and magnetic interactions at length scales inaccessible to first-principles methods. Here we introduce DSpinGNN, a bifurcated machine-learning architecture comprising an E(3)-equivariant graph neural network (E-GNN) for classical Langevin structural dynamics and a physics-informed Δ-MLP that maps instantaneous local Cr-I-Cr bond geometry to isotropic exchange couplings, with the Goodenough-Kanamori superexchange relationship embedded as an analytical inductive bias. Trained on 345 DFT+U configurations of monolayer CrI3 and evaluated on a strictly withheld 61-configuration test set, DSpinGNN simultaneously achieves an energy MAE of 1.1 meV/atom, a force MAE of 6.5 meV/Å, and an exchange coupling MAE of 0.18 meV (R2=0.91). Deployed at 400× scale in a 3,200-atom supercell under a collinear Ising-constrained adiabatic approximation at 5 K, the model maps the local exchange response to a propagating biaxial strain wave. Wave reflection at periodic boundaries generates transient constructive interference regions where local compressive strain exceeds the DFT-established FM-to-AFM threshold, producing spatially heterogeneous exchange coupling textures that damp as the wave dissipates. Quantitative analysis yields a domain wall width of ξ=1.7±0.3~nm and a constructive-interference oscillation period of τ=0.27~ps -- mesoscopic observables inaccessible to direct DFT and constituting testable predictions for cryogenic magnetic force microscopy. DSpinGNN provides a reproducible, transferable framework for mesoscale exchange mapping in strain-driven 2D magnetic materials.

Video analysis software is a staple of the modern undergraduate physics laboratory, enabling students to extract quantitative kinematic data from real-world phenomena. In this paper, we introduce PhysTrackX, a modern, open-source video analysis framework designed to minimize cognitive load and maximize pedagogical value. Built with a streamlined graphical interface, PhysTrackX features a comprehensive set of functionalities including video trimming, spatial calibration, and coordinate system definition with robust, machine learning based tracking. Furthermore, it introduces an integrated optical character recognition (OCR) module for synchronizing kinematic motion with external digital sensor displays, an interactive geometry plugin for in-situ spatial measurements, and a data management tool. In this article, we introduce the software and its functionality through some case studies.