Quantifying the Magnetic Interactions Governing Chiral Spin Textures Using Deep Neural Networks.

The interplay of magnetic interactions in chiral multilayer films gives rise to nanoscale topological spin textures that form attractive elements for next-generation computing. Quantifying these interactions requires several specialized, time-consuming, and resource-intensive experimental techniques. Imaging of ambient domain configurations presents a promising avenue for high-throughput extraction of parent magnetic interactions. Here, we present a machine learning (ML)-based approach to simultaneously determine the key magnetic interactions─symmetric exchange, chiral exchange, and anisotropy─governing the chiral domain phenomenology in multilayers, using a single binarized image of domain configurations. Our convolutional neural network model, trained and validated on over 10,000 domain images, achieved R2 > 0.85 in predicting the parameters and independently learned the physical interdependencies between magnetic parameters. When applied to microscopy data acquired across samples, our model-predicted parameter trends are consistent with those of independent experimental measurements. These results establish ML-driven techniques as valuable, high-throughput complements to conventional determination of magnetic interactions and serve to accelerate materials and device development for nanoscale electronics.

Unveiling the Emergent Traits of Chiral Spin Textures in Magnetic Multilayers.

Magnetic skyrmions are topologically wound nanoscale textures of spins whose ambient stability and electrical manipulation in multilayer films have led to an explosion of research activities. While past efforts focused predominantly on isolated skyrmions, recently ensembles of chiral spin textures, consisting of skyrmions and magnetic stripes, are shown to possess rich interactions with potential for device applications. However, several fundamental aspects of chiral spin texture phenomenology remain to be elucidated, including their domain wall (DW) structure, thermodynamic stability, and morphological transitions. Here the evolution of these textural characteristics are unveiled on a tunable multilayer platform-wherein chiral interactions governing spin texture energetics can be widely varied-using a combination of full-field electron and soft X-ray microscopies with numerical simulations. With increasing chiral interactions, the emergence of Néel helicity, followed by a marked reduction in domain compressibility, and finally a transformation in the skyrmion formation mechanism are demonstrated. Together with an analytical model, these experiments establish a comprehensive microscopic framework for investigating and tailoring chiral spin texture character in multilayer films.

Giant intrinsic photoresponse in pristine graphene.

When the Fermi level is aligned with the Dirac point of graphene, reduced charge screening greatly enhances electron-electron scattering1-5. In an optically excited system, the kinematics of electron-electron scattering in Dirac fermions is predicted to give rise to novel optoelectronic phenomena6-11. In this paper, we report on the observation of an intrinsic photocurrent in graphene, which occurs in a different parameter regime from all the previously observed photothermoelectric or photovoltaic photocurrents in graphene12-20: the photocurrent emerges exclusively at the charge neutrality point, requiring no finite doping. Unlike other photocurrent types that are enhanced near p-n or contact junctions, the photocurrent observed in our work arises near the edges/corners. By systematic data analyses, we show that the phenomenon stems from the unique electron-electron scattering kinematics in charge-neutral graphene. Our results not only highlight the intriguing electron dynamics in the optoelectronic response of Dirac fermions, but also offer a new scheme for photodetection and energy harvesting applications based on intrinsic, charge-neutral Dirac fermions.

Particle Collisions and Negative Nonlocal Response of Ballistic Electrons.

An electric field that builds in the direction against current, known as negative nonlocal resistance, arises naturally in viscous flows and is thus often taken as a telltale of this regime. Here, we predict negative resistance for the ballistic regime, wherein the ee collision mean free path is greater than the length scale at which the system is being probed. Therefore, negative resistance alone does not provide strong evidence for the occurrence of the hydrodynamic regime; it must thus be demoted from the rank of irrefutable evidence to that of a mere forerunner. Furthermore, we find that negative response is log enhanced in the ballistic regime by the physics related to the seminal Dorfman-Cohen log divergence due to memory effects in the kinetics of dilute gases. The ballistic regime therefore offers a unique setting for exploring these interesting effects due to electron interactions.