No Arabic abstract
The high-entropy alloys Al$_{x}$CrFeCoNi exist over a broad range of Al concentrations ($0 < x < 2$). With increasing Al content their structure is changed from the fcc to bcc phase. We investigate the effect of such structural changes on transport properties including the residual resistivity and the anomalous Hall resistivity. We have performed a detailed comparison of the first-principles simulations with available experimental data. We show that the calculated residual resistivities for all studied alloy compositions are in a fair agreement with available experimental data as concerns both the resistivity values and concentration trends. We emphasize that a good agreement with experiment was obtained also for the anomalous Hall resistivity. We have completed study by estimation of the anisotropic magnetoresistance, spin-disorder resistivity, and Gilbert damping. The obtained results prove that the main scattering mechanism is due to the intrinsic chemical disorder whereas the effect of spin polarization on the residual resistivity is appreciably weaker.
The lattice dynamics for NiCo, NiFe, NiFeCo, NiFeCoCr, and NiFeCoCrMn medium to high entropy alloy have been investigated using the DFT calculation. The phonon dispersions along three different symmetry directions are calculated by the weighted dynamical matrix (WDM) approach and compared with the supercell approach and inelastic neutron scattering. We could correctly predict the trend of increasing of the vibrational entropy by adding the alloys and the highest vibrational entropy in NiFeCoCrMn high entropy alloy by WDM approach. The averaged first nearest neighbor (1NN) force constants between various pairs of atoms in these intermetallic are obtained from the WDM approach. The results are discussed based on the analysis of these data.
Multi-principal-element alloys, including high-entropy alloys, experience segregation or partially-ordering as they are cooled to lower temperatures. For Ti$_{0.25}$CrFeNiAl$_{x}$, experiments suggest a partially-ordered B2 phase, whereas CALculation of PHAse Diagrams (CALPHAD) predicts a region of L2$_{1}$+B2 coexistence. We employ first-principles density-functional theory (DFT) based electronic-structure approach to assess stability of phases of alloys with arbitrary compositions and Bravais lattices (A1/A2/A3). In addition, DFT-based linear-response theory has been utilized to predict Warren-Cowley short-range order (SRO) in these alloys, which reveals potentially competing long-range ordered phases. The resulting SRO is uniquely analyzed using concentration-waves analysis for occupation probabilities in partially-ordered states, which is then be assessed for phase stability by direct DFT calculations. Our results are in good agreement with experiments and CALPHAD in Al-poor regions ($x le 0.75$) and with CALPHAD in Al-rich region ($0.75 le {x} le 1$), and they suggest more careful experiments in Al-rich region are needed. Our DFT-based electronic-structure and SRO predictions supported by concentration-wave analysis are shown to be a powerful method for fast assessment of competing phases and their stability in multi-principal-element alloys.
The unprecedented wide bandgap tunability (~1 eV) of Al$_x$In$_{1-x}$As$_y$Sb$_{1-y}$ latticed-matched to GaSb enables the fabrication of photodetectors over a wide range from near-infrared to mid-infrared. In this paper, the valence band-offsets in AlxIn1-xAsySb1-y with different Al compositions are analyzed by tight-binding calculations and X-ray photoelectron spectroscopy (XPS) measurements. The observed weak variation in valence band offsets is consistent with the lack of any minigaps in the valence band, compared to the conduction band.
Generative deep learning is powering a wave of new innovations in materials design. In this article, we discuss the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships in order to generate novelty on demand, making them a valuable tool for materials informatics.
The study of alloys using computational methods has been a difficult task due to the usually unknown stoichiometry and local atomic ordering of the different structures experimentally. In order to combat this, first-principles methods have been coupled with statistical methods such as the Cluster Expansion formalism in order to construct the energy hull diagram, which helps to determine if an alloyed structure can exist in nature. Traditionally, density functional theory (DFT) has been used in such workflows. In this work we propose to use chemically accurate many-body variational Monte Carlo (VMC) and diffusion Monte Carlo (DMC) methods to construct the energy hull diagram of an alloy system, due to the fact that such methods have a weaker dependence on the starting wavefunction and density functional, scale similarly to DFT with the number of electrons, and have had demonstrated success for a variety of materials. To carry out these simulations in a high-throughput manner, we propose a method called Jastrow sharing, which involves recycling the optimized Jastrow parameters between alloys with different stoichiometries. We show that this eliminates the need for extra VMC Jastrow optimization calculations and results in a significant computational cost savings (on average 1/4 savings of total computational time). Since it is a novel post-transition metal chalcogenide alloy series that has been synthesized in its few-layer form, we used monolayer $GaS_xSe_{1-x}$ as a case study for our workflow. By extensively testing our Jastrow sharing procedure for monolayer $GaS_xSe_{1-x}$ and quantifying the cost savings, we demonstrate how a pathway towards chemically accurate high-throughput simulations of alloys can be achieved using many-body VMC and DMC methods.