Research

Ultrafast light-matter interaction provides a compelling platform to explore various unprecedented phenomena in materials, such as light-induced magnetic or ferroelectric hidden phases and topologically nontrivial defects. In particular, the ultrahigh field in the intense ultrashort laser pulses could bring materials out of their equilibrium, with the recovery dynamics disclosing the inner nature of novel interplays of lattice, spins, and charges. Some of these intrinsic interactions are like “Silent Genes”: in equilibrium phases, they neither give energy gains nor penalties, and thus do not deliver phenomenal behavior in thermal equilibrium states; however, they can be excited/adjusted by light and drive materials from their ground state to other phases, including some that cannot be typically reached under equilibrium conditions. Thus, understanding the “hidden genes/energies” and how lights couple to them can unveil routes to selectively induce or control desired properties of materials at pico-/femtoseconds paces, such as light-triggered magnetization, ferroelectric re-orientation, giant conductivity change, etc.

Fig. 1 Ab-initio-based effective Hamiltonian method implemented in LINVARIANT. (a) rather intertwined Hamiltonian in realistic materials; (b) effective models written in elementary excitations, such as phonons, magnons, and entangled orbitals, and their intrinsic interactions; (c) materials property change due to the light-activated/adjusted inner couplings.

However, a systematic understanding of the individual interactions in the optic-driven phenomena remains challenging because of the rather intertwined Hamiltonian of realistic materials, hindering the materials’ application for future technologies.

Our group develops and uses the ab-initio-based effective Hamiltonian method (see fig. 1) to study ultrafast light-matter interactions in functional and quantum materials. Specifically, our group seeks to (i) build connections between light-induced phenomena in experiments and the theoretical understanding of intrinsic interactions; (ii) design strategies to trigger material functionality change on demand with custom-designed lights that can extrinsically activate or adjust those intrinsic interactions; and (iii) push the boundaries of comprehensive theoretical and computational frameworks to describe ultrafast phenomena. Our works aim to contribute to new technologies in which only a gentle flick can selectively trigger materials’ property change.

The application of physics-based artificial intelligence (AI) and multiscale methods has the potential to greatly enhance the simulation and discovery of functional materials, such as post-silicon materials, optoelectronics, energy storage, and energy conversion materials, among others.

However, developing physics-based models that accurately represent real materials poses a significant challenge.

LINVARIANT involves the integration of an AI Copilot (see figure above) to assist in material modeling and the characterization of the underlying interactions responsible for their properties. It is important to note that our approach maintains a physics-based framework rather than relying solely on machine learning (ML) models, which are often considered "black boxes." This means that AI will serve as a co-pilot in our exploration of materials, complementing our investigations rather than replacing the model itself. Nevertheless, ML-type models will be accessible and can be transformed into transparent physical models.

Spintronics with Ferroelectrics

Today’s memory electronics are draining up the world’s electricity due to the power-intensive process of magnetization reversal by spin transfer torques, especially as transistor sizes shrink to the nanometer regime.

This is driving research on low-power electric-field control of magnetization and conductivity. Ferroelectrics, which possess a polarization order parameter, offer a promising solution since their electric-field-induced switching typically consumes 1,000 times less energy than magnetization switching. Moreover, ferroelectrics can substantially modify the charges across the interface, thereby non-volatilely tuning the properties of adjacent materials. As a result, ferroelectrics are among the most promising materials for energy-efficient, memory, and logic devices beyond CMOS.

Over the past decade, the emergence of topological electric states in polar oxides has been a significant development, displaying a range of non-trivial structures such as vortices, skyrmions, merons, and labyrinthine textures. These electric counterparts to exotic spin textures were not anticipated for a long time due to the high dipolar energy cost.

However, we have recently established the existence of electric Dzyaloshinskii-Moriya interaction and its electronic origin, indicating the intrinsic possibility of creating local energy minimums of non-trivial topological states. Given that magnetic DMI has fertilized the study of the topological spin states, eDMI will provide the fertile backdrop for the dawn of the polar topological era.

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