Topological polarization textures in ferroelectrics offer pathways to dense memory, neuromorphic computing, and controlled probes of topology in solids. In rhombohedral barium titanate, theory has identified stable antiskyrmions of topological charge -2 that fractionalize into six -1/3 fractional hotspots, termed topological quarks. Here we extend this landscape to Zr-substituted barium titanate (BZT) using a first-principles parameterized effective-Hamiltonian framework. In an ordered 12.5% composition, the chemically doubled periodicity enforces an alternation along [111]: one half hosts the -2 antiskyrmion (six -1/3 quarks), the other a +4 skyrmion (six +2/3 quarks). The two share the same six-vortex skeleton but differ by an integer +1 per vortex in the plane-integrated slice charge. In random BZT, nanodomains remain inducible and cryogenically stable, yet quenched disorder pins and distorts the vortices, producing a heterogeneous, skyrmion-glass like state with fluctuations of the topological charge along the axis. Thermal stability maps show that pure BT retains -2 textures up to ~100 K, whereas in BZT the critical temperature is nonmonotonic, with a minimum near 6-8% Zr, reflecting competition between ferroelectric softening and disorder pinning. Importantly, the 12.5% ordered arrangement remains rhombohedral above 300 K, enabling field-stabilized nanodomains at 293 K. Under a local [111] bias, the ordered system carries +4 slice charge, while the random composition fragments under the same conditions. These results establish BZT as a platform for chemically programmed, fractionalized ferroelectric topology from cryogenic to room temperature and suggest routes to multistate, reconfigurable devices.

V$_{1-x}$Al$_x$ is a representative example of highly resistive metallic alloys exhibiting a crossover to a negative temperature coefficient of resistivity (TCR), known as the Mooij correlation. Despite numerous proposals to explain this anomalous behavior,none have provided a satisfactory quantitative explanation thus far. In this work, we calculate the electrical conductivity using an ab initio methodology that combines the Kubo-Greenwood formalism with the coherent potential approximation (CPA). The temperature dependence of the conductivity is obtained within a CPA-based model of thermal atomic vibrations. Using this approach, we observe the crossover to the negative TCR behavior in V$_{1-x}$Al$_x$, with the temperature coefficient following the Mooij correlation, which matches experimental observations in the intermediate-to-high temperature this http URL of the results allows us to clearly identify a non-Boltzmann contribution responsible for this behavior and describe it as a function of temperature and composition.

We have measured the critical current density, superconducting coherence length, and superconducting transition temperature of single-domain, epitaxially-grown Nb(110)/Au(111)/Nb(110) trilayers, all of which show a non-monotonic dependence on the thickness of the Au layer. These results are compared with the predictions of a relativistic, ab-initio theory, which incorporates superconducting correlations. We find good agreement with experiment, coming from a rich interplay between superconducting proximity - and quantum size effects, mediated by Andreev bound states. These results suggest that quantum size effects could provide a systematic method of controlling the transport properties of superconducting multilayers.

The impact of an applied electric field on the exchange coupling parameters has been investigated based on first-principles electronic structure calculations by means of the KKR Green function method. The calculations have been performed for a Fe film, free-standing and deposited on two different substrates, having 1 monolayer (ML) thickness to minimize the effect of screening of the electric field typical for metallic systems. By comparing the results for the free-standing Fe ML with those for Fe on the various substrates, we could analyze the origin of the field-induced change of the exchange interactions. Compared to the free-standing Fe ML, in particular rather pronounced changes have been found for the Fe/Pt(111) system due to the localized electronic states at the Fe/Pt interface, which are strongly affected by the electric field and which play an important role for the Fe-Fe exchange interactions.

Topological polarization textures in ferroelectrics offer pathways to dense memory, neuromorphic computing, and controlled probes of topology in solids. In rhombohedral barium titanate, theory has identified stable antiskyrmions of topological charge -2 that fractionalize into six -1/3 fractional hotspots, termed topological quarks. Here we extend this landscape to Zr-substituted barium titanate (BZT) using a first-principles parameterized effective-Hamiltonian framework. In an ordered 12.5% composition, the chemically doubled periodicity enforces an alternation along [111]: one half hosts the -2 antiskyrmion (six -1/3 quarks), the other a +4 skyrmion (six +2/3 quarks). The two share the same six-vortex skeleton but differ by an integer +1 per vortex in the plane-integrated slice charge. In random BZT, nanodomains remain inducible and cryogenically stable, yet quenched disorder pins and distorts the vortices, producing a heterogeneous, skyrmion-glass like state with fluctuations of the topological charge along the axis. Thermal stability maps show that pure BT retains -2 textures up to ~100 K, whereas in BZT the critical temperature is nonmonotonic, with a minimum near 6-8% Zr, reflecting competition between ferroelectric softening and disorder pinning. Importantly, the 12.5% ordered arrangement remains rhombohedral above 300 K, enabling field-stabilized nanodomains at 293 K. Under a local [111] bias, the ordered system carries +4 slice charge, while the random composition fragments under the same conditions. These results establish BZT as a platform for chemically programmed, fractionalized ferroelectric topology from cryogenic to room temperature and suggest routes to multistate, reconfigurable devices.

To study martensitic phase transformation we use a micromechanical model based on statistical mechanics. Employing lattice Monte-Carlo simulations and realistic material properties for shape-memory alloys (SMA), we investigate the combined influence of the external stress, temperature, and interface energy between the austenitic and martensitic phase on the transformation kinetics and the effective material compliance. The one-dimensional model predicts well many features of the martensitic transformation that are observed experimentally. Particularly, we study the influence of the interface energy on the transformation width and the effective compliance. In perspective, the obtained results might be helpful for the design of new SMAs for more sensitive smart structures and more efficient damping systems.

Antiskyrmions, as topological quasi-particles, hold significant promise for spintronics and nanoscale data storage applications. Using molecular dynamics simulations based on effective Hamiltonians, we investigate the thermal stability of antiskyrmion nanodomains in rhombohedral barium titanate. At 1 K, antiskyrmions with a topological charge of -2 emerge as the most stable nanodomain state across all examined diameters. In our systematic study, the most robust antiskyrmion was found to have a diameter of 4 nm, maintaining its original size, shape, and topological charge up to the characteristic temperature T* ~ 85 K. Domains with diameters between 2.8 and approximately 4.5 nm exhibit fragmentation into six topological defects, termed quarks, each carrying a fractional skyrmion charge of -1/3. For domains larger than 4.5 nm, each topological quark splits into two pre-quarks, each with a charge of -1/6. These larger nanodomains demonstrate increased mobility and a growing tendency for shape and skyrmion charge fluctuations. Above the T* temperature, all larger nanodomains gradually shrink to a diameter of about 4 nm before collapsing into a single-domain state. These findings reveal the relatively high stability of antiskyrmions over a broad temperature range, even in the absence of a stabilizing bias field, and emphasize the pivotal role of topological quark dynamics. This establishes barium titanate as a key platform for exploring and applying topological phenomena.

High entropy alloys (HEA) represent a class of materials with promising properties, such as high strength and ductility, radiation damage tolerance, etc. At the same time, a combinatorially large variety of compositions and a complex structure render them quite hard to study using conventional methods. In this work, we present a computationally efficient methodology based on ab initio calculations within the coherent potential approximation. To make the methodology predictive, we apply an exchange-correlation correction to the equation of state and take into account thermal effects on the magnetic state and the equilibrium volume. The approach shows good agreement with available experimental data on bulk properties of solid solutions. As a particular case, the workflow is applied to a series of iron-group HEA to investigate their solid solution strengthening within a parameter-free model based on the effective medium representation of an alloy. The results reveal intricate interactions between alloy components, which we analyze by means of a simple model of local bonding. Thanks to its computational efficiency, the methodology can be used as a basis for an adaptive learning workflow for optimal design of HEA.

We developed a method for fitting machine-learning interatomic potentials with magnetic degrees of freedom, namely, magnetic Moment Tensor Potentials (mMTP). The main feature of our method consists in fitting mMTP to magnetic forces (negative derivatives of energies with respect to magnetic moments) as obtained spin-polarized density functional theory calculations. We test our method on the bcc Fe-Al system with different compositions. Specifically, we calculate formation energies, equilibrium lattice parameter, and total cell magnetization. Our findings demonstrate an accurate correspondence between the values calculated with mMTP and those obtained by DFT at zero temperature. Additionally, using molecular dynamics, we estimate the finite-temperature lattice parameter and capture the cell expansion as was previously revealed in experiment. Furthermore, we demonstrate that fitting to magnetic forces increases the reliability of structure relaxation (or, equilibration), in the sense of ensuring that every relaxation run ends up with a successfully relaxed structure (the failure may otherwise be caused by falsely driving a configuration away from the region covered in the training set).

While first-principles methods have been successfully applied to characterize individual properties of multi-principal element alloys (MPEA), their use to search for optimal trade-offs between competing properties is hampered by high computational demands. In this work, we present a novel framework to explore Pareto-optimal compositions by integrating advanced ab initio-based techniques into a Bayesian multi-objective optimization workflow complemented with a simple analytical model providing straightforward analysis of trends. We benchmark the framework by applying it to solid solution strengthening and ductility of refractory MPEAs, with the parameters of the strengthening and ductility models being efficiently computed using a combination of the coherent-potential approximation method, accounting for finite-temperature effects, and actively-learned moment-tensor potentials parameterized with ab initio data. Properties obtained from ab initio calculations are subsequently used to extend predictions of all relevant material properties to a large class of refractory alloys with the help of the analytical model validated by the data and relying on a few element-specific parameters and universal functions that describe bonding between elements. Our findings offer new crucial insights into the traditional strength-vs-ductility dilemma of refractory MPEAs. The proposed framework is versatile and can be extended to other materials and properties of interest, enabling a predictive and tractable high-throughput screening of Pareto-optimal MPEAs over the entire composition space.

There are various methods for modeling phase transformations in materials science, including general classes of phase-field methods and reactive diffusion methodologies, which most importantly differ in their treatment of interface energy. These methodologies appear mutually exclusive since the respective numerical schemes only allow for their primary use case. To address this issue, a novel methodology for modeling phase transformations in multi-phase, multi-component systems, with particular emphasis on applications in materials science and the study of substitutional alloys is introduced. The fundamental role of interface energy in the evolution of a material's morphology will be studied by example of binary and ternary systems. Allowing full control over the interface energy quantity enables more detailed investigations and bridges the gaps between known methods. We prove the thermodynamic consistency of the derived method and discuss several use cases, such as vacancy-mediated diffusion. Furthermore a scheme for relating Onsager and Diffusion coefficients is proposed, which allows us to study the intricate coupling that is observed in multicomponent systems. We hope to contribute to the development of new mathematical tools for modeling complex phase transformations in materials science.

The lattice Boltzmann method (LBM) is a numerical approach to tackle problems described by a Boltzmann type-equation, where time, space, and velocities are discretized to describe scattering and advection. Even though the LBM executes advection along a lattice direction without numerical error, its usage in the high Knudsen number regime (ballistic) has been hindered by the ray effect problem (for dimensions greater than 1D). This problem has its origin in the low number of available propagation directions on standard LBM lattices. Here, to overcome this limitation, we propose the worm-lattice Boltzmann method (worm-LBM), which allows a high number of lattice directions by alternating in time the basic directions described within the next neighbor schemes. Additionally, to overcome the velocity anisotropy issue, which otherwise clearly manifests itself in the ballistic regime (e.g. the $\sqrt 2$ higher grid velocity of the D2Q8 scheme along the diagonal direction compared to the axial one), the time-adaptive scheme (TAS) is proposed. The TAS method makes use of pausing advection on the grid, allowing to impose not only isotropic propagation but also arbitrary direction-dependent grid velocity. Last but not least, we propose a grid-mean free path (grid-MFP) correction to correctly handle the aforementioned velocity issue in the diffusive limit, without affecting the ballistic one. We provide a detailed description of the TAS method and the worm-LBM algorithm, and verify their numerical accuracy by using several transient diffusive-ballistic phonon transport cases, including different initial and boundary conditions. Overall, the new, very accurate, and efficient worm-LBM algorithm, free of numerical smearing and false scattering, has the potential to be at the forefront of the numerical solvers to tackle the advective part of different equations in a wide field of applications.

In this work, we investigate the dislocation-impurity interaction energies and their profiles for various \textit{3d} elements \textemdash V, Cr, Mn, Cu, Ni, and Co \textemdash in and around $1/2\langle111\rangle$ screw dislocations in $\alpha$-Fe using \textit{ab initio} methods. We consider the ferromagnetic and paramagnetic states, with the latter being modeled through both the disordered local moment model and a spin-wave approach. Our findings reveal that (1) magnetic effects are large compared to size misfit effects of substitutional impurities, and (2) dislocation-impurity interactions are dependent on the magnetic state of the matrix and thermal lattice expansion. In particular, Cu changes from core-attractive in the ferromagnetic state to repulsive in the paramagnetic state.

We present a protocol for automated fitting of magnetic Moment Tensor Potential explicitly including magnetic moments in its functional form. For the fitting of this potential we use energies, forces, stresses, and magnetic forces (negative derivatives of energies with respect to magnetic moments) of configurations selected with an active learning algorithm. These selected configurations are computed using constrained density functional theory, which enables calculating energies and their derivatives for both equilibrium and non-equilibrium (excited) magnetic states. We test our protocol on the system of B1-CrN and demonstrate that the automatically trained magnetic Moment Tensor Potential reproduces mechanical, dynamical, and thermal properties, of B1-CrN in the paramagnetic state with respect to density functional theory and experiments.

Atomistically-informed phase field simulations have been performed to investigate the effect of five common alloying elements (Nb, Ti, Mo, V, Mn) on austenite grain growth. The anisotropic simulations based on the segregation energy profiles of the solutes to four different grain boundary (GB) types from density functional theory calculations suggest a secondary role of solute drag anisotropy on grain growth. Hence, the solute trends are determined to be the same for all investigated GBs, and as a result, the $\Sigma 5(310)[001]$ GB can be considered as a representative GB for solute trend predictions. The decrease in grain growth rates due to solute additions is quantitatively described using a solute trend parameter. The following hierarchy of the solute's effectiveness to retard austenite grain growth has been determined based on the results of the presented model calculations in agreement with the experimental observations: Nb$>$Ti$>$Mo$>$V$\approx$Mn. The limitations and the strengths of the proposed approach are discussed in detail, and a potential application of this approach to steel design is proposed.

Solute segregation in alloys is a key phenomenon which affects various material characteristics such as embrittlement, grain growth and precipitation kinetics. In this work, the segregation energies of Y, Zr, and Nb to a \textgreek{S}5 grain boundary in a bcc Ti-25 at \% Mo alloy were determined using density functional theory (DFT) calculations. A systematic approach was laid out by computing the solution energy distributions in the bulk alloy using Warren-Cowley short-range order parameters to find a representative bulk-solute reference energy. Additionally, different scenarios were considered when a solute atom replaces different sites in terms of their local Ti-Mo chemistry at the GB plane to calculate the distribution of segregation energies. The solute segregation to a Mo site at the GB plane is preferred rather than to a Ti site. Further analysis shows that these segregation energy trends can be rationalized based on a primarily elastic interaction. Thus the segregation energies scale with the solute size such that Y has the largest segregation energies followed by Zr and Nb.

In this study, we investigate the effect of incorporating explicit dispersion interactions in the functional form of machine learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential for accurate modeling of liquid carbon tetrachloride, methane, and toluene. We show that explicit incorporation of dispersion interactions via D2 and D3 corrections significantly improves the accuracy of MLIPs when the cutoff radius is set to a commonly used value of 5 -- 6 \r{A}. We also show that for carbon tetrachloride and methane, a substantial improvement in accuracy can be achieved by extending the cutoff radius to 7.5 \r{A}. However, for accurate modeling of toluene, explicit incorporation of dispersion remains important. Furthermore, we find that MLIPs incorporating dispersion interactions via D2 reach a close level of accuracy to those incorporating D3, and D2 is suitable for accurate modeling of the systems in the study, while being less computationally expensive. We evaluated the accuracy of MLIPs in dimer binding curves compared to ab initio data and in predicting density and radial distribution functions compared to experiments.

To gain a deeper insight into the anomalous yield behavior of Ni3Al, it is essential to obtain temperature-dependent formation Gibbs energies of the relevant planar defects. Here, the Gibbs energy of the complex stacking fault (CSF) is evaluated using a recently proposed ab initio framework [Acta Materialia, 255 (2023) 118986], accounting for all thermal contributions - including anharmonicity and paramagnetism - up to the melting point. The CSF energy shows a moderate decrease from 300K to about 1200 K, followed by a stronger drop. We demonstrate the necessity to carefully consider the individual thermal excitations. We also propose a way to analyze the origin of the significant anharmonic contribution to the CSF energy through atomic pair distributions at the CSF plane. With the newly available high-temperature CSF data, an increasing energy barrier for the cross-slip process in Ni3Al with increasing temperature is unveiled, necessitating the refinement of existing analytical models.

It is demonstrated that thermally induced longitudinal spin fluctuations (LSF) play an important role in itinerant Co$_3$Mn$_2$Ge at an elevated temperature. The effect of LSF is taken into account during {\it ab initio} calculations via a simple model for the corresponding entropy contribution. We show that the magnetic entropy leads to the appearance of a medium size local moment on Co atoms. As a consequence, this leads to a renormalization of the magnetic exchange interactions with a quite substantial impact upon the calculated Curie temperature. Taking LSF into account, the calculated Curie temperature can be brought to be in good agreement with the experimental value.

In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the Moment Tensor Potential and Equivariant Tensor Network potential. We show that explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules.