Materials Science
We investigate the macrospin-to-vortex (MS-to-V) transition in Co-based artificial spin-vortex ice (ASVI) in the presence of perpendicular magnetic anisotropy (PMA) by spin-wave spectroscopy. Detailed micromagnetic simulations using mumax3…
Chiral $\pi$-conjugated polymers are an attractive material platform for spin polarized carrier-transport and spectroscopy, but fundamental considerations for how torsional disorder influences the response properties of the material have…
Chirality, a central concept across many scientific disciplines, continues to inspire the discovery of novel physical phenomena. In condensed matter physics, structural chirality - defined by the absence of mirror plane symmetries - has…
We present a bulk boundary condition formalism for surface calculations in Kohn--Sham density functional theory. The approach exploits the nearsightedness of electronic interactions in real space to restrict the calculation to a localized…
Twisted atomically thin layers have attracted much attention for Moir\'e potential and correlated quantum phenomena. However, existing Moir\'e superlattices have largely been limited to extensive wavefunction without lateral confinement.…
We present electric-field-modulated electron paramagnetic resonance (EFM-EPR) measurements on centrosymmetric single crystals of the molecular spin triangle $\mathrm{[{Fe_3}O({O_2}CPh){_6}(py){_3}]ClO{_4}{\cdot}py}$ ($\bf{Fe_3}$). We…
In magnetically ordered materials, magnetic field and temperature variations modify the magnetic texture through their coupling to the local energy landscape, imprinting distinct fingerprints in the resulting magnetic domain patterns.…
Most MLIP benchmarks reward static accuracy while ignoring inference efficiency and hardware scalability -- driving model bloat with unclear real-world value. We benchmark 23 mainstream open-source MLIPs on a low-cost NVIDIA DGX Spark (128…
The negatively charged boron vacancy center in hexagonal boron nitride is a premier candidate for quantum sensing, yet its performance is critically limited by longitudinal spin-lattice relaxation time ($T_1$). A microscopic understanding…
Universal machine learning interatomic potentials (MLIPs) are foundation AI models transforming atomistic simulations, but their practical use remains hindered by fragmented software ecosystems, dependency conflicts, and the lack of…
Although Large Language Models (LLM) and Artificial Intelligence (AI) tools have enabled a rapid increase in the generation rate of predicted materials, the rate of new materials discovery has lagged behind. This is due to the challenges…
Gallium nitride (GaN) transistors have become the platform of choice for power electronics and radio-frequency power amplifiers. To unlock capabilities beyond those of conventional GaN, integrating ferroelectric heterostructure has been…
We study non-Hermitian antiferromagnetic resonance in an antiferromagnetic insulator/nonmagnetic metal junction with sublattice-dependent damping and spin-orbit torque. By formulating the linearized Landau-Lifshitz-Gilbert (LLG) equation as…
We present the approach needed to calculate stress within density functional theory (DFT) using a localised orbital basis, both for exact diagonalisation and linear scaling approaches, and demonstrate our implementation within the large…
Understanding the thermodynamic properties of disordered magnetic alloys requires a unified treatment of configurational (chemical, spin, etc.) and structural degrees of freedom, which has remained beyond the scope of existing computational…
SrTiO3 thin films, 20-30nm, with high quality crystal structure and low roughness can be used as growth templates for complex oxides or as the dielectric materials for capacitor structures. In this work, stoichiometric STO thin films were…
Quasi-2D Bi$_2$O$_2$Se is part of an intensive materials research effort aimed at finding new semiconductors that outperform silicon-based electronics in terms of speed and power consumption. This material exhibits exceptionally high…
Graph neural networks have become the dominant machine-learning architecture for predicting materials properties from crystal structures. Yet the initialization of atomic node features has received comparatively little attention, and…
Altermagnets, which combine antiferromagnetic-like magnetic compensation with ferromagnetic-like broken time-reversal symmetry, hold great promise for high-density and ultrafast spintronic applications. However, the detection and switching…
A topotactic phase transition involves the transformation of one crystalline solid to another, which may include the loss or gain of material, where the orientation of the parent crystal determines the orientation of the daughter. We set…