In multicomponent materials, short-range order (SRO) is the development of correlated arrangements of atoms at the nanometer scale. Its impact in compositionally complex materials has stimulated an intense debate within the materials science community. Understanding SRO is critical to control the properties of technologically relevant materials, from metallic alloys to functional ceramics. In contrast to long-range order, quantitative characterization of the nature and spatial extent of SRO evades most of the experimentally available techniques. Simulations at the atomistic scale have full access to SRO but face the challenge of accurately sampling high-dimensional configuration spaces to identify the thermodynamic and kinetic conditions at which SRO is formed and what impact it has on material properties. Here we highlight recent progress in computational approaches, such as machine learning-based interatomic potentials, for quantifying and understanding SRO in compositionally complex materials. We briefly recap the key theoretical concepts and methods.

Explicit anharmonicity, defined as the vibrational contribution beyond the quasiharmonic approximation, is qualitatively different between the group V and group VI bcc refractory elements. Group V elements show a small and mostly negative anharmonic entropy, whereas group VI elements have a large positive anharmonic entropy, strongly increasing with temperature. Here, we explain this difference utilizing highly accurate anharmonic free energies and entropies from ab initio calculations for Nb and Ta (group V), and Mo and W (group VI). The numerically calculated entropies are in agreement with prior experimental data. The difference in behavior between the two sets of elements arises not from their high-temperature behavior but rather from the 0K quasiharmonic reference state. We understand this by analyzing the 0K and the high-temperature phonon density of states and the electronic density of states. The qualitative difference disappears when the anharmonicity is instead referenced with a high-temperature effective harmonic potential. However, even for an optimized effective harmonic reference, the remaining effective anharmonicity is significant. The reason is that the anharmonicity in the bcc systems - carried by asymmetric distributions in the nearest neighbors - can never be accounted for by a harmonically restricted potential.

We present an analysis of temperature-dependent atomic short-range ordering and phase stability of the face-centered cubic CrCoNi medium-entropy alloy employing a combination of ab initio calculations and on-lattice machine learning interatomic potentials. Temperature-dependent properties are studied with canonical Monte Carlo simulations. At around 975 K a phase transition into an ordered Cr(Ni,Co)2 phase (MoPt2-type) is found. This hitherto not reported state has an ordering energy twice as large than the ordered structures previously suggested. We show that magnetism is not responsible for the observed chemical ordering.

The microstructure of advanced high-strength steels often shows a sensitive dependence on alloying. For example, adding Cr to improve the corrosion resistance of medium-Mn steels also enhances the precipitation of carbides. The current study focuses on the behavior of H in such complex multicomponent carbides by employing different methodological strategies. We systematically analyze the impact of Cr, Mn, and Fe using density functional theory (DFT) for two prototype precipitate phases, M3C and M23C6, where M represents the metal sublattice. Our results show that the addition of these alloying elements yields strong nonmonotonic chemical trends for the H solubility. We identify magnetovolume effects as the origin for this behavior, which depend on the considered system, the sites occupied by H, and short- vs long-range interactions between H and the alloying elements. We further show that the H solubility is directly correlated with the occupation of its nearest-neighbor shells by Cr and Mn. Based on these insights, DFT data from H containing binary-metal carbides are used to design a ridge regression based model that predicts the solubility of H in the ternary-metal carbides (Fe-Cr-Mn-C).

Since its first emergence in 2004, the high-entropy alloy (HEA) concept has aimed at stabilizing single- or dual-phase multi-element solid solutions through high mixing entropy. Here, this strategy is changed and renders such massive solid solutions metastable, to trigger spinodal decomposition for improving the alloys’ magnetic properties. The motivation for starting from a HEA for this approach is to provide the chemical degrees of freedom required to tailor spinodal behavior using multiple components. The key idea is to form Fe-Co enriched regions which have an expanded volume (relative to unconstrained Fe-Co), due to coherency constraints imposed by the surrounding HEA matrix. As demonstrated by theory and experiments, this leads to improved magnetic properties of the decomposed alloy relative to the original solid solution matrix. In a prototype magnetic FeCoNiMnCu HEA, it is shown that the modulated structures, achieved by spinodal decomposition, lead to an increase of the Curie temperature by 48% and a simultaneous increase of magnetization by 70% at ambient temperature as compared to the homogenized single-phase reference alloy. The findings thus open a pathway for the development of advanced functional HEAs.

The phase stability of a bcc AlNbTiV high-entropy alloy at elevated temperatures is studied using a combination of machine-learning interatomic potentials, first-principles calculations, and Monte Carlo simulations. The simulations reveal a B2 ordering below about 1700 K, mainly caused by a strong site preference of Al and Ti. A much weaker site preference for V and Nb is observed, strongly affecting the alloys total configurational entropy. The underlying mechanisms of the B2 phase stability as opposed to the random solid solution are discussed in terms of a high persisting configurational entropy of the B2 phase due to strong sublattice site disorder.

Recent experiments show that the chemical composition of body-centered cubic (bcc) refractory high entropy alloys (HEAs) can be tuned to enable transformation-induced plasticity (TRIP), which significantly improves the ductility of these alloys. This calls for an accurate and efficient method to map the structural stability as a function of composition. A key challenge for atomistic simulations is to separate the structural transformation between the bcc and the ω phases from the intrinsic local lattice distortions in such chemically disordered alloys. To solve this issue, we develop a method that utilizes a symmetry analysis to detect differences in the crystal structures. Utilizing this method in combination with ab initio calculations, we demonstrate that local lattice distortions largely affect the phase stability of Ti–Zr–Hf–Ta and Ti–Zr–Nb–Hf–Ta HEAs. If relaxation effects are properly taken into account, the predicted compositions near the bcc–hcp energetic equilibrium are close to the experimental compositions, for which good strength and ductility due to the TRIP effect are observed.

The interface method is a well established approach for predicting melting points of materials using interatomic potentials. However, applying the interface method is tedious and involves significant human intervention. The whole procedure involves several successive tasks: estimate a rough melting point, set up the interface structure, run molecular dynamic calculations and analyze the data. Loop calculations are necessary if the predicted melting point is different from the estimated one by more than a certain convergence criterion, or if full melting/solidification occurs. In this case monitoring the solid–liquid phase transition in the interface structure becomes critical. As different initial random seeds for the molecular dynamic simulations within the interface method induce slightly different melting points, a few ten or hundred interface method calculations with different random seeds are necessary for performing a statistical analysis on these melting points. Considering all these technical details, the work load for manually executing and combining the various involved scripts and programs quickly becomes prohibitive. To simplify and automatize the whole procedure, we have implemented the interface method into pyiron (http://pyiron.org). Our fully automatized procedure allows to efficiently and precisely predict melting points of stable unaries represented by arbitrary potentials with only two user-specified parameters (interatomic potential file and element). For metastable or dynamically unstable unary phases, the crystal structure needs to be provided as an additional parameter. We have applied our automatized approach on fcc Al, Ni, dynamically unstable bcc Ti and hcp Mg and employed a large set of available interatomic potentials. Melting points for classical interatomic potentials of these metals have been obtained with a numerical precision well below 1 K.

An active learning approach to train machine-learning interatomic potentials (moment tensor potentials) for multicomponent alloys to ab initio data is presented. Employing this approach, the disordered body-centered cubic (bcc) TiZrHfTax system with varying Ta concentration is investigated via molecular dynamics simulations. Our results show a strong interplay between elastic properties and the structural ω phase stability, strongly affecting the mechanical properties. Based on these insights we systematically screen composition space for regimes where elastic constants show little or no temperature dependence (elinvar effect).

We reveal the impact of magnetic ordering on stacking fault energy (SFE) and its influence on the deformation mechanisms and mechanical properties in a class of nonequiatomic quinary Mn-containing compositional complex alloys or high entropy alloys (HEAs). By combining ab initio simulation and experimental validation, we demonstrate magnetic ordering as an important factor in the activation and transition of deformation modes from planar dislocation slip to TWIP (twinning-induced plasticity) and/or TRIP (transformation-induced plasticity). A wide compositional space of Cr20MnxFeyCo20Niz(x+y+z=60, at. %) was probed by density-functional theory calculations to search for potential alloys displaying the TWIP/TRIP effects. Three selected promising HEA compositions with varying Mn concentrations were metallurgically synthesized, processed, and probed for microstructure, deformation mechanism, and mechanical property evaluation. The differences in the deformation modes of the probed HEAs are interpreted in terms of the computed SFEs and their dependence on the predicted magnetic state, as revealed by ab initio calculations and validated by explicit magnetic measurements. It is found that the Mn content plays a key role in the stabilization of antiferromagnetic configurations which strongly impact the SFEs and eventually lead to the prevalent deformation behavior.

Local lattice distortions in a series of body-centered cubic alloys, including refractory high-entropy alloys, are investigated by means of atomic volumes, atomic charges, and atomic stresses defined by the Bader charge analysis based on first-principles calculations. Analyzing the extensive data sets, we find large distributions of these atomic properties for each element in each alloy, indicating a large impact of the varying local chemical environments. We show that these local-environment effects can be well understood and captured already by the first and the second nearest neighbor shells. Based on this insight, we employ linear regression models up to the second nearest neighbor shell to accurately predict these atomic properties. Finally, we find that the elementwise-averaged values of the atomic properties correlate linearly with the averaged valence-electron concentration of the considered alloys.

Concentrated solid solutions including the class of high entropy alloys (HEAs) have attracted enormous attention recently. Among these alloys a recently developed face-centered cubic (fcc) equiatomic VCoNi alloy revealed extraordinary high yield strength, exceeding previous high-strength fcc CrCoNi and FeCoNiCrMn alloys. Significant lattice distortions had been reported in the VCoNi solid solution. There is, however, a lack of knowledge about potential short-range order (SRO) and its implications for most of these alloys. We performed first-principles calculations and Monte Carlo simulations to compute the degree of SRO for fcc VCoNi, namely, by utilizing the coherent-potential approximation in combination with the generalized perturbation method as well as the supercell method in combination with recently developed machine-learned potentials. We analyze the chemical SRO parameters as well as the impact on other properties such as relaxation energies and lattice distortions.

The lattice dynamics in magnetic materials, such as Fe depends on the degree of disorder of the atomic magnetic moments and the time scale of spin fluctuations. Using first-principles methods, we have studied this effect by determining the force constant matrix in two limits: (i) When spin fluctuations are much faster than the atom vibrations, their combined impact is captured by a spin-space averaged force constant matrix, (ii) when individual spin fluctuations are sufficiently slow to scatter the phonon modes, the itinerant coherent potential approximation with spin-pair resolved force constants (i.e., φ↑↑,φ↓↓, and φ↑↓) is employed in this paper. The physical consequences for the vibrational spectral functions are analyzed by systematically modifying the input parameters (magnetization and ratio of force constants betweens atoms with equal and opposite spin directions) and by deriving them for the prototype material system bcc Fe from first-principles calculations. In the paramagnetic regime, the two limits yield identical phonon spectra. Below the Curie temperature, however, there are regions in the parametric study that show qualitative differences, including a broadening of the phonon peaks. For bcc Fe, however, the quantitative modifications of phonon frequencies turn out to be small.

We apply the efficient two-optimized references thermodynamic integration using Langevin dynamics method [Phys. Rev. B 96, 224202 (2017)2469-995010.1103/PhysRevB.96.224202] to calculate highly accurate melting properties of Al and magnetic Ni from first principles. For Ni we carefully investigate the impact of magnetism on the liquid and solid free energies including longitudinal spin fluctuations and the reverse influence of atomic vibrations on magnetic properties. We show that magnetic fluctuations are effectively canceling out for both phases and are thus not altering the predicted melting temperature. For both elements, the generalized gradient approximation (GGA) and the local-density approximation (LDA) are used for the exchange-correlation functional revealing a reliable ab initio confidence interval capturing the respective experimental melting point, enthalpy of fusion, and entropy of fusion.

To facilitate the understanding of Invar effects and design of FeNiCo-base Invar alloys characterized by low thermal expansion coefficient (TEC), we investigated the magnetic and thermal expansion behavior of an equiatomic prototype medium entropy alloy FeNiCo and a non-equiatomic (super Invar)Fe₆₃Ni₃₂Co₅ (at. %)reference alloy by means of experiments and ab initio calculations. Both alloys consist of a single face-centered cubic phase with fully recrystallized microstructure in the homogenized state. Large spontaneous volume magnetostriction is observed in both alloys below their respective Curie temperatures. The Invar effect in the non-equiatomic Fe₆₃Ni₃₂Co₅ alloy is of step-type with nearly zero TEC over a wide temperature range (from room temperature to 120 °C)below its Curie temperature. The equiatomic FeNiCo alloy shows a peak-type Invar effect in a very narrow temperature range (from ∼675 °C to ∼730 °C)with relatively low TECs. The equiatomic FeNiCo alloy shows both higher saturation magnetization and Curie temperature than the non-equiatomic Fe₆₃Ni₃₂Co₅ alloy. The relationships among magnetic behavior, spontaneous volume magnetostriction and Invar effects for a wider array of metallic alloys are discussed mainly based on Masumoto's rule combined with Wohlfarth's itinerant electron theory. An Invar alloy search map is constructed based on the present results and available literature data to visualize the relationships among saturation magnetization, Curie temperature and thermal expansion coefficient for a wide range of Invar alloys. Based on this treasure map a design route for further developments of new Invar alloys by tuning their magnetic properties is discussed.

Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.

The unique and unanticipated properties of multiple principal component alloys have reinvigorated the field of alloy design and drawn strong interest across scientific disciplines. The vast compositional parameter space makes these alloys a unique area of exploration by means of computational design. However, as of now a method to compute efficiently, yet with high accuracy the thermodynamic properties of such alloys has been missing. One of the underlying reasons is the lack of accurate and efficient approaches to compute vibrational free energies—including anharmonicity—for these chemically complex multicomponent alloys. In this work, a density-functional-theory based approach to overcome this issue is developed based on a combination of thermodynamic integration and a machine-learning potential. We demonstrate the performance of the approach by computing the anharmonic free energy of the prototypical five-component VNbMoTaW refractory high entropy alloy.

Severe lattice distortion is a core effect in the design of multiprincipal element alloys with the aim to enhance yield strength, a key indicator in structural engineering. Yet, the yield strength values of medium- and high-entropy alloys investigated so far do not substantially exceed those of conventional alloys owing to the insufficient utilization of lattice distortion. Here it is shown that a simple VCoNi equiatomic medium-entropy alloy exhibits a near 1 GPa yield strength and good ductility, outperforming conventional solid-solution alloys. It is demonstrated that a wide fluctuation of the atomic bond distances in such alloys, i.e., severe lattice distortion, improves both yield stress and its sensitivity to grain size. In addition, the dislocation-mediated plasticity effectively enhances the strength–ductility relationship by generating nanosized dislocation substructures due to massive pinning. The results demonstrate that severe lattice distortion is a key property for identifying extra-strong materials for structural engineering applications.

The temperature-dependent intrinsic stacking fault Gibbs energy is computed based on highly converged density-functional-theory (DFT) calculations for the three prototype face-centered cubic metals Al, Cu, and Ni. All relevant temperature-dependent contributions are considered including electronic, vibrational, magnetic, and explicit anharmonic Gibbs energy contributions as well as coupling terms employing state-of-the-art statistical sampling techniques. Particular emphasis is put on a careful comparison of different theoretical concepts to derive the stacking fault energy such as the axial-next-nearest-neighbor-Ising (ANNNI) model or the vacuum-slab approach. Our theoretical results are compared with an extensive set of previous theoretical and experimental data. Large uncertainties in the experimental data highlight the necessity of complementary parameter-free calculations. Specifically, the temperature dependence is experimentally unknown and poorly described by thermodynamic databases. Whereas calphad derived data shows an increase of the stacking fault energy with temperature for two of the systems (Cu and Ni), our results predict a decrease for all studied systems. For Ni, the temperature induced change is in fact so strong that in the temperature interval relevant for super-alloy applications the stacking fault energy falls below one third of the low temperature value. Such large changes clearly call for a revision of the stacking fault energy when modeling or designing alloys based on such elements.