Materials Science (cond-mat.mtrl-sci)

Abstract: Acting as a highly nonlinear response to the strong laser field, high harmonic generation (HHG) naturally contains the fingerprints of atomic and electronic properties of materials. Electronic properties of a solid such as band structure and topology can thus be probed, while the phonon dynamics during HHG are often neglected. Here we show that by exploiting the effects of phonon deformation on HHG, the intrinsic phonon information can be deciphered and direct probing of band- and mode-resolved electron-phonon couplings (EPC) of photoexcited materials is possible. Considering HHG spectroscopy can be vacuum free and unrestricted to electron occupation, this work suggests HHG is promising for all-optical characterization of EPC in solids, especially for gapped quantum states or materials under high pressure.

Abstract: Recently, there has been a rapidly growing interest in two-dimensional (2D) transition metal chalcogenide monolayers (MLs) due to their unique magnetic and electronic properties. By using an evolutionary algorithm and first-principles calculations, we report the discovery of a previously unexplored, chemically, energetically, and thermodynamically stable 2D antiferromagnetic (AFM) CrSe ML with a N\'eel temperature higher than room temperature. Remarkably, we predict an electric field-controllable metal-insulator transition (MIT) in a van der Waals (vdW) heterostructure comprised of CrSe ML and ferroelectric Sc2CO2. This tunable transition in CrSe/Sc2CO2 heterostructure is attributed to the change in the band alignment between CrSe and Sc2CO2 caused by the ferroelectric polarization reversal in Sc2CO2. Our findings suggest that 2D AFM CrSe ML has important potential applications in AFM spintronics, particularly in the gate voltage conducting channel.

Abstract: In recent years, halide perovskite materials have been used to make high performance solar cell and light-emitting devices. However, material defects still limit device performance and stability. Here, we use synchrotron-based Bragg Coherent Diffraction Imaging to visualise nanoscale strain fields, such as those local to defects, in halide perovskite microcrystals. We find significant strain heterogeneity within MAPbBr$_{3}$ (MA = CH$_{3}$NH$_{3}^{+}$) crystals in spite of their high optoelectronic quality, and identify both $\langle$100$\rangle$ and $\langle$110$\rangle$ edge dislocations through analysis of their local strain fields. By imaging these defects and strain fields in situ under continuous illumination, we uncover dramatic light-induced dislocation migration across hundreds of nanometres. Further, by selectively studying crystals that are damaged by the X-ray beam, we correlate large dislocation densities and increased nanoscale strains with material degradation and substantially altered optoelectronic properties assessed using photoluminescence microscopy measurements. Our results demonstrate the dynamic nature of extended defects and strain in halide perovskites and their direct impact on device performance and operational stability.

Abstract: Atomic simulations hold significant value in clarifying crucial matters such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the magnetic moment. Nonetheless, precisely portraying the interaction energy and its impact on lattice and spin-driving forces, such as atomic force and magnetic torque, remains a formidable task in the computational domain. Consequently, there is no atomic-scale approach capable of elucidating the evolution of lattice and spin at the same time in magnetic materials. Addressing this knowledge deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with advanced deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials.

Abstract: A self-consistent stochastic model of domain structure formation in a uniaxial ferroelectric, quenched from a high-temperature paraelectric phase to a low-temperature ferroelectric phase, is developed with an account of the applied electric field and the feedback effect via local depolarization fields. Both polarization and field components are considered as Gauss random variables. A system of integro-differential equations for correlation functions of all involved variables is derived and solved analytically and numerically. Phase diagram in terms of the average value and dispersion of polarization reveals different possible equilibrium states and available final single-domain and multi-domain states. The time-dependent evolution of the average polarization and dispersion discloses a bifurcation behavior and the temperature-dependent value of the electric field, deciding between the single-domain and multi-domain final states, which can be interpreted as the coercive field. Analytical and numerical results for the time-dependent correlation length and correlation functions exhibit plausible agreement with available experimental data.

Abstract: We present a study on the transport and materials properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperature and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes computational limitations of DFT-MD simulations, enabling us to study transport and materials properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular-dynamics (MD) by examining the thermal conductivity, diffusion coefficient, viscosity, sound velocity, and ion-ion structure factor of aluminum up to about 60,000 K, where we find good agreement with previous theoretical data.

Abstract: We demonstrate that magnetoresistance of creeping charge-density waves in the quasi-one dimensional conductor {\it o}-TaS$_3$ changes its character from a negative parabolic at $T\gtrsim 10$ K where it obeys $1/T^2$ law to a weakly temperature dependent negative nearly linear one at lower temperatures. The dominant contribution into the negative parabolic magnetoresistance comes from magnetic field induced splitting of the CDW order parameter. The linear magnetoresistance arises due to CDW quantum interference similar to the scenario of negative linear magnetoresistance in single-electron systems.