Abstract
Highentropy materials have attracted considerable interest due to the combination of useful properties and promising applications. Predicting their formation remains the major hindrance to the discovery of new systems. Here we propose a descriptor—entropy forming ability—for addressing synthesizability from first principles. The formalism, based on the energy distribution spectrum of randomized calculations, captures the accessibility of equallysampled states near the ground state and quantifies configurational disorder capable of stabilizing highentropy homogeneous phases. The methodology is applied to disordered refractory 5metal carbides—promising candidates for highhardness applications. The descriptor correctly predicts the ease with which compositions can be experimentally synthesized as rocksalt highentropy homogeneous phases, validating the ansatz, and in some cases, going beyond intuition. Several of these materials exhibit hardness up to 50% higher than rule of mixtures estimations. The entropy descriptor method has the potential to accelerate the search for highentropy systems by rationally combining first principles with experimental synthesis and characterization.
Introduction
Highentropy materials having a highly disordered homogeneous crystalline single phase (potentially stabilized entirely by entropic contributions) continue to attract a great deal of research interest^{1,2,3}. Remarkable properties have been reported: high strength (yield stress >1 GPa) combined with ductility^{4,5,6,7,8,9,10,11}, hardness^{5,12,13}, superconductivity^{14}, colossal dielectric constant^{15}, and superionic conductivity^{16}. Entropy is thought to play a keystabilizing role in highentropy alloys^{1}, entropystabilized oxides^{17,18}, highentropy borides^{19}, and highentropy carbides^{20,21,22,23}. The latter three classes consist of disordered metal cation sublattices with several species at equiconcentration combined with oxide^{15,17,18,24}, boride^{19}, or carbide^{20,21,22,23} anion sublattices. These systems offer the potential to combine excellent thermomechanical properties and resilient thermodynamic stability given by entropy stabilization with the higher oxidation resistance of ceramics^{25}. The resistance of disordered carbides to extreme heat^{26,27,28}, oxidation^{23}, and wear makes them promising ultrahightemperature ceramics for thermal protection coatings in aerospace applications^{29}, and as highhardness, relatively lowdensity highperformance drill bits and cutting tools in mining and industry.
Superhard transition metal carbides have been known since the 1930s to exhibit significant levels of solid solution^{26,30,31}, and to display high melting temperatures^{26,27}. Ta_{x}Hf_{1−x}C forms a homogeneous solid solution across all composition ranges^{28,32,33}, with Ta_{4}HfC_{5} exhibiting one of the highest experimentally reported melting points (\(T_{\mathrm{m}}\sim 4263\,{\mathrm{K}}\)^{26,27}). In this case, the two refractory metals randomly populate one of the two rocksalt sublattices^{28}. More recent measurements indicate that the maximum melting point of 4232 K occurs without Ta at the composition HfC_{0.98}^{34}. Investigation of new carbide compositions will help elucidate the high temperature behavior of these materials, and will provide an avenue to settle the discrepancies in the experimental literature. To discover materials with even more advantageous properties, including increased thermal stability, enhanced strength and hardness, and improved oxidation resistance^{23}, more species and configurations have to be considered. Unfortunately, the lack of a rational, effective, and rapid method to find and characterize the disordered crystalline phase makes it impossible to pinpoint the right combination of species/compositions and the discovery continues by slow and relatively expensive trial and error.
Computationally, the hindrance in insilico disordered materials development can be attributed to entropy—a very difficult quantity to parameterize when searching through the immense space of candidates (even with efficient computational methods, e.g., Monte Carlo and abinitio lattice energies in the WangLandau^{35} or nested sampling^{36} formalisms). CALPHAD has also been applied successfully^{2,13,37,38,39}, although it is dependent on the availability of sufficient experimental data. This is the perfect challenge for abinitio highthroughput computing^{40} as long as reasonable entropy descriptors—the set of parameters capturing the underlying mechanism of a materials property—can be found.
In this article, we undertake the challenge by formulating an entropyformingability (EFA) descriptor. It captures the relative propensity of a material to form a highentropy singlephase crystal by measuring the energy distribution (spectrum) of configurationally randomized calculations up to a given unitcell size. A narrow spectrum implies a low energy cost for accessing metastable configurations, hence promoting randomness (i.e., entropy) into the system (highEFA) at finite temperature. In contrast, a wide spectrum suggests a composition with a high energy barrier for introducing different configurations (lowEFA), and thus with a strong preference for ordered phases. The method is benchmarked by the matrix of possible carbides. Given a set of eight refractory metals (Hf, Nb, Mo, Ta, Ti, V, W, and Zr) plus carbon, the formalism predicts the matrix of synthesizable fivemetal highentropy carbides. Candidates are then experimentally prepared, leading to a novel class of systems. In particular, it is demonstrated that the descriptor is capable of reliably distinguishing between the compositions that easily form homogeneous single phases and the ones that do not, including identifying compositions that form single phases despite incorporating multiple binary carbide precursors with different structures and stoichiometric ratios. Note that because of the differing stoichiometries of the nonrocksalt phase binary carbide precursors Mo_{2}C and W_{2}C, the compositions listed in this work are nominal. There are extensive carbon vacancies in the anion sublattice in the synthesized materials, further contributing to the configurational entropy. Several of these materials display enhanced mechanical properties, e.g., Vickers hardness up to 50% higher than predicted by a rule of mixtures (ROM). Thus, this class of materials has strong potential for industrial uses where dense and wearresistant impactors are needed, particularly for extreme temperature applications. The successful outcome demonstrates the strength of the synergy between thermodynamics, highthroughput computation, and experimental synthesis.
Results
EFA formalism
To accelerate the search in the chemical space, the entropy content of a compound is estimated from the energy distribution spectrum of metastable configurations above the zerotemperature ground state. At finite T, any disordered state can be present with a probability given by the Boltzmann distribution and the state’s degeneracy. Note that configurations are randomly sampled up to a given unitcell size: the larger the size, the more accurately the spectrum represents the real thermodynamic density of states.
The energy distribution (H_{i}) spectrum can be quantitatively characterized by its standard deviation σ, so that the σ becomes the descriptor for S: the smaller σ, the larger S. The descriptor for an Nspecies system, called the EFA, is defined as the inverse of the σ of the energy spectrum above the ground state of the Nsystem at zero temperature:
where
where n is the total number of sampled geometrical configurations and g_{i} are their degeneracies. H_{mix} is the mixedphase enthalpy approximated by averaging the enthalpies H_{i} of the sampled configurations:
The broader the spectrum, the more energetically expensive it will be to introduce configurational disorder into the system, and thus the lower the EFA. EFA is measured in (eV/atom)^{−1}.
EFA calculation
A total of 56 fivemetal compositions can be generated using the eight refractory metals (8!/5!3! = 56) of interest (Hf, Nb, Mo, Ta, Ti, V, W, and Zr). For each composition, the Hermite normal form superlattices of the Automatic FLOW (AFLOW) partial occupation (AFLOWPOCC) method^{41} generate 49 distinct 10atomcell configurations, resulting in a total of 2744 configurations needed to determine the EFA of this composition space (Methods section). The abinitio calculated EFA values for the full set of 56 fivemetal compositions are provided in Table 1. Nine candidates are chosen from this list for experimental synthesis and investigation: (i) the three candidates with the highest value of EFA (MoNbTaVWC_{5} (EFA = 125 (eV/atom)^{−1}), HfNbTaTiZrC_{5} (EFA = 100 (eV/atom)^{−1}), HfNbTaTiVC_{5} (EFA = 100 (eV/atom)^{−1}), high probability of forming highentropy single phases), (ii) the two candidates with the lowest value of EFA (HfMoVWZrC_{5} (37 (eV/atom)^{−1}), HfMoTiWZrC_{5} (38 (eV/atom)^{−1}), low probability of forming highentropy single phases), and (iii) four chosen at random with intermediate EFA (NbTaTiVWC_{5} (77 (eV/atom)^{−1}), HfNbTaTiWC_{5} (67 (eV/atom)^{−1}), HfTaTiWZrC_{5} (50 (eV/atom)^{−1}), and HfMoTaWZrC_{5} (45 (eV/atom)^{−1})). Figure 1a shows the energy distribution and EFA values obtained ab initio from configurations generated with AFLOW for the nine chosen systems. For MoNbTaVWC_{5}, HfNbTaTiZrC_{5}, and HfNbTaTiVC_{5}, most of the configurations are within 20 meV/atom of the lowest energy state, and the distributions have an EFA of at least 100 (eV/atom)^{−1}. Therefore, at finite temperature, most of the configurations should have a high probability of being formed, so that a high level of configurational randomness is expected to be accessible in the three systems, making them promising candidates to form a highentropy homogeneous single phase. Achieving a similar level of configurational randomness would be progressively more difficult in NbTaTiVWC_{5}, HfNbTaTiWC_{5}, and HfTaTiWZrC_{5} as the different configurations display a broader energy distribution, with EFAs ranging from 50 to 77 (eV/atom)^{−1}. A higher energy cost is needed to incorporate configurational entropy into these three compositions, so forming a homogeneous single phase will be more difficult. For HfMoTaWZrC_{5}, HfMoTiWZrC_{5}, and HfMoVWZrC_{5}, the spread of energies for the configurations is very wide, with EFA values from 45 down to 37 (eV/atom)^{−1}. These materials would be expected to be very difficult to synthesize as a homogeneous single phase.
Competing ordered phases
The phase diagrams for the fivemetal carbide systems were generated to investigate the existence of binary and ternary ordered structures that could compete with the formation of the highentropy single phase. First, prototypes for experimentally reported binary and ternary carbide structures^{42,43} are used to calculate the formation enthalpies of additional ordered phases for the AFLOW database. The results are used to generate the convex hull phase diagrams for all 56 compositions using the AFLOWCHULL module^{44}. The relevant binary and ternary convex hulls are illustrated in Supplementary Figures 1–36. The distance along the enthalpy axis of the lowest energy AFLOWPOCC configuration from the convex hull, ΔH_{f}, is listed in Table 1 (the decomposition reaction products are summarized in Supplementary Table 2). A rough estimation of the synthesis temperature T_{s} (see the analogous EntropyStabilized Oxides case, Fig. 2 of ref.^{17})—can be calculated by dividing ΔH_{f} by the ideal configuration entropy (Supplementary Table 1, with the ideal entropy per atom evaluated as 0.5k_{B} × log0.2, since k_{B} × log0.2 is the entropy per metal carbide atomic pair). A precise characterization of the disorder requires more expensive approaches, such as the LTVC method^{45}, and is beyond the scope of this article. The highest temperature is 2254 K, which is less than the synthesis temperature of 2200 °C (2473 K), indicating that during sintering the disordered phases are thermodynamically accessible with respect to decomposition into ordered compounds. In analogy to the formation of metallic glasses where energetic confusion obstructs crystalline growth^{46,47} once the temperature is reduced, systems with high EFA remain locked into ensembles of highly degenerate configurations, retaining the disorder achieved at high temperature. Hence, EFA provides a measure of the relative synthesizability of the disordered composition.
From enthalpy to entropy
It is important to consider that the metal carbide precursors have very strong covalent/ionic bonds, and are therefore enthalpy stabilized^{48}. However, the same might not be the case for their mixture. In fact, a statistical analysis of the AFLOW.org enthalpies^{44} indicates that the gain in formation enthalpy by adding mixing species, ΔH_{f}(N + 1) − ΔH_{f}(N), decreases with N, and can easily be overcome by the monotonic increase in entropygain for the disordered systems (to go from order to complete or partial disorder). In the AFLOW analysis, the threshold between low and highentropy systems is around four mixing species. To have completely entropystabilized materials, five mixing species are required (similar to the EntropyStabilized Oxides^{17}). For carbides in which only the metalsublattice is randomly populated, five metals should be enough to achieve carbide entropy stabilization, especially at equicomposition. Notably, if other sublattices were also allowed to contain disorder, (e.g., reciprocal systems like Ta_{x}Hf_{1−x}C_{1−y}^{26,27}), then the overall number of species might reduce. In this example, entropy was increased by introducing point defects (vacancies)—a promising strategy to improve high temperature performance. In HfC_{1−x}, the reduction of C to substoichiometry enhances the stabilizing effect of the configurational entropy on the solid phase, offsetting its Gibbs free energy (vacancies can only exist in the solid phase) leading to an overall increase in melting point (see ref.^{28}).
Experimental results
To validate the predictions, the nine chosen carbides are experimentally synthesized and characterized (see Methods section). The chemical homogeneity of each sample is measured using energydispersive Xray spectroscopy (EDS), whereas the crystalline structure is determined via Xray diffraction (XRD).
An example of the evolution of a sample of composition HfNbTaTiVC_{5} through each processing step is given in Fig. 2a, demonstrating the densification and homogenization into a single rocksalt structure. At least three distinct precursor phases are distinguishable in the mixed powder pattern. Following ball milling, the individual phases are still present, however, the peaks are considerably broadened, which is due to particle size reduction and mechanical alloying. Following the final spark plasma sintering (SPS) step at 2200 °C, the sample consolidates into a bulk solid pellet of the desired single rocksalt phase indicating the successful synthesis of a highentropy homogeneous carbide.
Results of XRD analysis for each sample following SPS at 2200 °C, presented in Fig. 1b, demonstrate that compositions MoNbTaVWC_{5}, HfNbTaTiZrC_{5}, HfNbTaTiVC_{5}, HfNbTaTiWC_{5}, NbTaTiVWC_{5}, and HfTaTiWZrC_{5} (the top 6) only exhibit single fcc peaks of the desired highentropy phase (rocksalt), whereas HfMoTaWZrC_{5}, HfMoTiWZrC_{5}, and HfMoVWZrC_{5} (the bottom 3) show multiple structures. The small peaks at 2θ = 31.7°, marked by the diamond symbol in the spectra of HfNbTaTiZrC_{5}, HfNbTaTiWC_{5}, and HfTaTiWZrC_{5} in Fig. 1b, are from the (111) plane of a monoclinic (Hf, Zr)O_{2} phase that remains due to processing. The volume fraction of this phase is <5% and does not significantly alter the composition of the carbide phase. The distinguishable second phase in HfMoTaWZrC_{5} is identified as a hexagonal phase. One and two secondary fcc phases are observed for HfMoVWZrC_{5} and HfMoTiWZrC_{5}, respectively. Microstructure analysis and selected elemental mapping (Figs. 2c–d) confirm that the systems displaying single phases are chemically homogeneous, whereas the multiphase samples undergo chemical segregation. For example, only grain orientation contrast is present in the HfNbTaTiZrC_{5} microstructure, and no indication of notable clustering or segregation is visible in its compositional maps. On the contrary, a clear chemical phase contrast is observable in the microstructure of the multiphase HfMoTaWZrC_{5} sample, and the compositional maps demonstrate that the secondary phase, apparent in XRD, is W and Morich.
Homogeneity analysis
Peak broadening in XRD patterns (Fig. 1b) is used to quantify the level of structural homogenization achieved in the samples. According to the Williamson–Hall formulation^{49}, broadening in XRD is principally due to crystallite size (∝ 1/cosθ, θ = Bragg angle) and lattice strain (∝ 1/tanθ). For multicomponent systems, significant broadening is expected to occur due to local lattice strains and variations in the interplanar spacings throughout the sample^{50}. The latter can be attributed to the inhomogeneous distribution of the elements, the extent of which can be evaluated by applying the analysis to a multicomponent system, which has only a single lattice structure and is assumed strain free^{50}.
The lattice distortion of the rocksalt phase in the singlephase materials (or the most prevalent in the multiphase ones) is determined by using the relationship between broadening β_{S} and Bragg angle θ (Methods section):
where ε is the lattice strain or variation in interplanar spacing due to chemical inhomogeneity, K is a constant (dependent on the grain shape), λ is the incident Xray wavelength, and D is the crystallite size. Since materials are assumed strain free, ε—obtained by inverting Eq. 4—represents the relative variation of the lattice parameter due to inhomogeneity. Thus, ε is both a measure of homogeneity and of the effective mixing with respect to the ideal scenario. The results for the EFA descriptor, the experimental characterization, as well as the values for ε for all nine carbides compositions are given in Table 1. The values for ε range from 0.063% for MoNbTaVWC_{5} and 0.094% for HfNbTaTiZrC_{5} (the most homogeneous materials) to 0.325% for HfMoVWZrC_{5} (the least homogeneous material). Overall, the experimental findings agree well with the predictions of EFA descriptor, validate its ansatz and indicate a potential threshold for our model of fivemetal carbides: EFA \(\sim 50 {\mathrm{(eV/atom)}}^{  1} \Rightarrow\) homogeneous disordered single phase (high entropy).
Highentropy synthesizability
The comparison between the EFA predictions and the homogeneity of the samples is analyzed in Fig. 2b. Although the Williamson–Hall formalism does not provide particularly accurate absolute values of ε, it is effective for comparing similarly processed samples, determining the relative homogeneity. The lattice distortion ε (capturing homogeneity) decreases linearly with the increase of EFA. The Pearson (linear) correlation of EFA^{−1} with ε is 0.97, whereas the Spearman (rank order) correlation is 0.98. As such, the EFA takes the role of an effective highentropy synthesizability descriptor.
Three facts are relevant. (i) Intuitively, several of the carbide compositions that easily form a highly homogeneous phase, particularly HfNbTaTiVC_{5} and HfNbTaTiZrC_{5}, come from binary precursors having the same structure and ratio of anions to cations as the final highentropy material. (ii) Counterintuitively, the highestEFA and most homogeneous phase MoNbTaVWC_{5} is made with two precursors having different structures and stoichiometric ratios from the highentropy material, specifically orthorhombic αMo_{2}C and hexagonal αW_{2}C, leading to a final substoichiometric MoNbTaVWC_{5−x}. The additional disorder provided by the presence of vacancies in the Csublattice is advantageous: it allows further entropy stabilization, potentially increasing the melting point visàvis the stoichiometric composition^{28}. An investigation of the effect of carbon stoichiometry is clearly warranted, although it is outside of the scope of this study. (iii) For tungsten and molybdenum, metalrich carbides are used because of the difficulties in obtaining molybdenum monocarbide (MoC) powder, or tungsten monocarbide (WC) powder in the particle sizes compatible with the other precursors, hindering consistent mixing, sintering and homogenization. It should be noted that additional samples of MoNbTaVWC_{5} were also synthesized using hexagonal WC with a smaller particle size, and the homogeneous rocksalt phase was again successfully obtained (see Supplementary Figure 37). The existence of phasepure MoNbTaVWC_{5} indicates that, similar to the rocksalt binary carbides, the multicomponent carbide is stable over a range of stoichiometry. From experimental/phenomenological grounds, the formation of such a phase is surprising. The equicomposition binary carbides MoC and WC have hexagonal ground states, and their rocksalt configurations have significantly higher formation enthalpy^{51}. Considering these facts, there are no experimental indications that adding Mo and W would contribute to stabilizing the most homogeneous rocksalt fivemetal carbide that was predicted by the EFA and later validated experimentally. The arguments demonstrate the advantage of a descriptor that quantifies the relative EFA over simple empirical/phenomenological rules, in that it correctly identifies this composition as having a high propensity to form a single phase, while simultaneously correctly predicting that several other systems having both Mo and W subcomponents undergo phase separation.
Mechanical properties
The Vickers hardness, H_{V}, and elastic modulus, E, of both the binary carbide precursors and the synthesized fivemetal singlephase compositions are measured using nanoindentation, where the properties are extracted from loaddisplacement curves (see Supplementary Figure 38(a)). The binary precursor samples for these measurements are prepared and analyzed using the same protocol to ensure the validity of the comparisons (see Methods section for more details). The results for the fivemetal compositions are in Table 2, whereas those for the binary carbides are listed in Table 3. It is found that, for the fivemetal compositions, the measured H_{V} and E values exceed those predicted from a ROM based on the binary precursor measurements. The enhancement of the mechanical properties is particularly strong in the case of HfNbTaTiZrC_{5}, where the measured E and H_{V} exceed the ROM predictions by 10 and 50%, respectively (see Supplementary Figure 38(b) for a comparison between the H_{V} results obtained from calculation, experiment, and ROM). Mass disorder is one possible source of the enhanced hardness: deformation is caused by dislocation movements and activation energy is absorbed and released at each lattice step. An ideal ordered system can be seen as a dislocationwaveguide with matched (uniform) impedance along the path: propagation occurs without any relevant energy reflection and/or dispersion. This is not the case for disordered systems: mass inhomogeneity causes impedance mismatch, generating reflections and disturbing the transmission by dispersing (scattering) its group energy. Macroscopically the effect is seen as increased resistance to plastic deformations—more mechanical work is required—i.e., increase of hardness. Other possible causes of increased hardness include solid solution hardening^{5,13,20}, where the atomic size mismatch results in lattice distortions, limiting the motion of dislocations necessary for plastic deformation; and changes in the slip systems and the ease with which slip can occur^{20,52}.
Elastic properties are calculated using AFLOW^{41,53} for the fivemetal compositions MoNbTaVWC_{5}, HfNbTaTiZrC_{5}, HfNbTaTiVC_{5}, HfNbTaTiWC_{5}, NbTaTiVWC_{5}, and HfTaTiWZrC_{5} (Table 2) and their precursors. In general, results are within the experimentally reported ranges for the binary carbides (Table 3 in the Methods section). H_{V} values are estimated from the bulk and shear moduli using the models introduced by Chen et al.^{54}, Teter^{55}, and Tian et al.^{56}. Computational models do not consider plastic deformation mechanisms in inhomogeneous systems and thus H_{V} predictions underestimate experiments, leading to results consistent with the ROM of binary carbides (Table 2). The outcome further corroborates that the experimentally observed enhancement of the mechanical properties is due to disorder.
Vibrational contribution to formation free energy
The vibrational contributions to the formation Gibbs free energy, ΔF_{vib}, at 2000 K are listed in Table 1 for the six compositions synthesized as a single phase. The vibrational free energies, F_{vib}, are calculated using the Debye–Grüneisen model implemented in the AFLOW–Automatic GIBBS Library (AGL) module^{57}, using the Poisson ratio calculated with AFLOW–Automatic Elasticity Library (AEL)^{53}. The average F_{vib} for the fivemetal compositions are calculated, weighted according to the Boltzmann distribution at 2000 K. The vibrational contribution to the formation Gibbs free energy, ΔF_{vib}, for each composition is obtained from the difference between its average F_{vib} and the average F_{vib} of its component binary carbides. ΔF_{vib} at 2000 K ranges from ~0 meV/atom for HfNbTaTiWC_{5} to −31 meV/atom for HfNbTaTiVC_{5}, which are significantly less than the total entropy contribution (mostly configurational plus vibrational) required to overcome the values of 50 meV/atom to 150 meV/atom for the formation enthalpy ΔH_{f}. These results are in agreement with previous observations that the vibrational formation entropy is generally an order of magnitude smaller than the configuration entropy^{39,58}.
Discussion
In this article, an EFA descriptor has been developed for the purpose of capturing synthesizability of highentropy materials. The framework has been applied to refractory metal carbides, leading to the prediction and subsequent experimental discovery of homogeneous highentropy single phases. The method is able to quantitatively predict the relative propensity of each composition to form a homogeneous single phase, thus identifying the most promising candidates for experimental synthesis. In particular, the experiments validate the prediction that the composition MoNbTaVWC_{5} should have a very high propensity to form a homogeneous single phase, despite incorporating both Mo_{2}C and W_{2}C, which have different structures (hexagonal and/or orthorhombic instead of rocksalt) and stoichiometric ratios from the fivemetal highentropy material.
Furthermore, it is demonstrated that disorder enhances the mechanical properties of these materials: HfNbTaTiZrC_{5} and HfTaTiWZrC_{5} are measured to have hardness of 32 GPa (almost 50% higher than the ROM prediction) and 33 GPa, respectively, suggesting a new avenue for designing superhard materials. The formalism could become the longsought enabler of accelerated design for highentropy functional materials with enhanced properties for a wide range of different technological applications.
Methods
Spectrum generation
The different possible configurations required to calculate the energy spectrum are generated using the AFLOWPOCC algorithm^{41} implemented within the AFLOW computational materials design framework^{51,59,60}. The algorithm initially generates a superlattice of the minimum size necessary to obtain the required partial occupancies within some userspecified accuracy. For each unique superlattice, the AFLOWPOCC algorithm then generates the complete set of possible supercells using Hermite normal form matrices^{41}. Nonunique supercell combinations are eliminated from the ensemble by first estimating the total energies of all configurations using a Universal Force Field^{41,61} based method, and then identifying duplicates from their identical energies.
Structure generation
In the case of the highentropy carbide^{17} systems investigated here, the AFLOWPOCC algorithm starts with the rocksalt crystal structure (spacegroup: \(Fm\bar 3m,\# 225\); Pearson symbol: cF8; AFLOW Prototype: AB_cF8_225_a_b^{62}) as the input parent lattice. Each anion site is occupied with a C atom (occupancy probability of 1.0), whereas the cation site is occupied by five different refractory metal elements, with a 0.2 occupancy probability for each. The AFLOWPOCC algorithm then generates a set of configurations (49 in total in the case of the rocksalt based fivemetal carbide systems, once structural duplicates are excluded), each containing 10 atoms: one atom of each of the metals, along with five carbon atoms. This is the minimum cell size necessary to accurately reproduce the required stoichiometry. All configurations have g_{i} = 10, except for one where g_{i} = 120, so that \(\mathop {\sum}\nolimits_{i = 1}^n g_i = 600\) for the rocksaltbased fivemetal carbide systems. Note that computational demands increase significantly with the number of elements: AFLOWPOCC generates 522, 1793, and 7483 for six, seven, and eightmetal carbide compositions, respectively.
Energies calculation
The energy of each configuration is calculated using density functional theory (Vienna Abinitio Simulation Package^{63}) within the AFLOW framework^{59} and the standard settings^{60}. Each configuration is fully relaxed using the Perdew, Burke, Ernzerhof (PBE) parameterization of the generalized gradient approximation exchangecorrelation functional^{64}, projector augmented wave potentials, at least 8000 kpoints per reciprocal atom (KPPRA), and a planewave cutoff of at least 1.4 times the cutoff values of constituent species’ pseudopotentials^{60}. The formation enthalpy (H_{f}) of each configuration along with the link to AFLOW.org entry page is provided in Supplementary Tables 4–10.
Mechanical properties
Elastic properties are calculated using the AEL module^{53} of the AFLOW framework, which applies a set of independent directional normal and shear strains to the structure, and fits the resulting stress tensors to obtain the elastic constants. From this, the bulk: B, and shear: G, moduli are calculated in the Voigt, Reuss and VoigtReussHill (VRH) approximations, with the average being used for the purposes of this work. The elastic or Young’s modulus: E, is calculated using the approximation E = 9BG/(3B + G), which can be derived starting from the expression for Hooke’s Law in terms of E and the Poisson ratio, ν: ε_{11} = 1/E[σ_{11} − ν(σ_{22} + σ_{33})]^{65}, and similarly for ε_{22} and ε_{33}. For a cubic system, ε_{11} = S_{11}σ_{11} + S_{12}σ_{22} + S_{12}σ_{33} (similarly for ε_{22} and ε_{33}), where S_{ij} are the elements of the elastic compliance tensor, so that 1/E = S_{11} and −ν/E = S_{12}. For a cubic system, the bulk modulus is B = 1/[3(S_{11} + 2S_{12})] = E/[3(1 − 2ν)]. The Poisson ratio can be written as ν = (3B − 2G)/(6B + 2G), and combining with the expression for B and rearranging gives the required E = 9BG/(3B + G).
The elastic properties for the fivemetal compositions are first calculated for each of the 49 configurations generated by AFLOWPOCC. The VRH approximated values of B and G for these configurations are listed in Supplementary Table 3, along with the AFLOWPOCC ensemble averaged electronic density of states (see Supplementary Figure 39). The average elastic moduli are then obtained, weighted according to the Boltzmann distribution at a temperature of 2200 °C (the experimental sintering temperature). These calculated values are compared with those obtained using a ROM (average of the binary components, weighted according to fractional composition in the sample).
Three different models are used for predicting the Vickers hardness based on the elastic moduli: Chen et al. (H_{V} = 2(k^{2}G)^{0.585} − 3; k = G/B)^{54}, Teter (H_{V} = 0.151G)^{55}, and Tian et al. (H_{V} = 0.92k^{1.137}G^{0.708}; k = G/B)^{56}. Note, however, that these models are based on the elastic response of the materials, and do not take into account phenomena such as plastic deformation, slip planes, and lattice defects.
Sample preparation
Initial powders of each of the eight binary precursor carbides (HfC, NbC, TaC, TiC, Mo_{2}C, VC, W_{2}C, ZrC) are obtained in >99% purity and −325 mesh (<44 μm) particle size (Alfa Aesar). Samples are weighed out in 15 g batches and mixed to achieve the desired fivemetal carbide compositions. To ensure adequate mixing, each sample is ball milled in a shaker pot mill for a total of 2 h in individual 30min intervals intersected by 10min rest times to avoid heating and consequent oxide formation. All milling is done in tungsten carbidelined stainless steel milling jars with tungsten carbide grinding media.
Bulk sample pellets are synthesized via solidstate processing routes. The fieldassisted sintering technique (FAST), also called SPS, is employed to simultaneously densify and react the compositions into singlephase materials. For all samples, sintering is done at 2200 °C with a heating rate of 100 °C/min, 30 MPa uniaxial pressure, and a 5min dwell at temperature. Samples are heated in vacuum atmosphere to 1300 °C followed by flowing argon to 2200 °C. All sintering is done in 20 mm graphite die and plunger sets with graphite foil surrounding the samples to prevent reaction with the die.
Sample analysis
Elemental analysis is performed using an FEI Quanta 600 SEM equipped with a Bruker eFlash EDS detector at an accelerating voltage of 20 kV. Microstructural scanning electron microscope (SEM) imaging is carried out using an FEI Apreo FESEM at an accelerating voltage of 5 kV, with a combination of secondary and backscattered electron detectors to show phase contrast. Crystal phase analysis is performed using a Rigaku Miniflex Xray Diffractometer with a stepsize of 0.02° and 5s dwells, using Cu Kα radiation (wavelength λ = 1.54059 Å) for all measurements and calculation of the lattice parameter. All sample patterns are fitted in Materials Data Incorporated’s (MDI) Jade 9 software^{66} with a residual of fit R < 8%. Lattice parameter, a_{exp}, values of 4.353 Å, 4.500 Å, 4.415 Å, 4.434 Å, 4.355 Å, 4.502 Å, 4.506 Å, 4.534 Å, and 4.476 Å were measured for MoNbTaVWC_{5}, HfNbTaTiZrC_{5}, HfNbTaTiVC_{5}, HfNbTaTiWC_{5}, NbTaTiVWC_{5}, HfTaTiWZrC_{5}, HfMoTaWZrC_{5}, HfMoVWZrC_{5}, and HfMoTiWZrC_{5}, respectively (for multiphase samples, a_{exp} refers to the primary cubic phase).
For analysis of sample peak broadening β_{S}, instrumental broadening β_{I} must first be determined. For this, a NIST 660b LaB_{6} standard is run under the same conditions as each carbide sample. The instrumental profile is then fitted, and β_{I} is determined to vary with Bragg angle θ as:
β_{S} is determined by subtracting β_{I} from the measured broadening β_{M}: \(\beta _{\mathrm{S}}^x = \beta _{\mathrm{M}}^x  \beta _I^x\). β_{M} is measured as a function of θ, and x is a constant between 1.0 and 2.0. In the current analysis, x is set to 2.0 due to the Gaussianlike shape of the instrument peaks, as this value leads to the lowest standard deviation of linear fits to the peak broadening data.
Both crystallite size and lattice strain contribute to β_{S}^{49,67,68:}
where ε is the lattice strain or variation in interplanar spacing due to chemical inhomogeneity, K is a constant (dependent on the grain shape), λ is the incident Xray wavelength and D is the crystallite size. Rearranging Eq. 6 gives:
The slope of a linear fit to the plot of β_{S}cosθ against sinθ is equal to the strain, or lattice distortion, whereas the yintercept of a linear fit with zero slope determines the crystallite size.
Mechanical testing
Mechanical properties of each of the singlephase compositions are tested using a Keysight NanoIndenter G200 with a Berkovich indenter tip. To rule out indentation size effects, testing is carried out at loads of both 50 mN and 300 mN, and no significant deviation in hardness or modulus is observed. To allow valid crosscomparison, each of the high entropy carbides is compared with the binary carbides, which were hotpressed and indentation tested under identical conditions. For the reported values, tests are carried out according to the standard method outlined in ISO 14577 using a maximum load of 50 mN. Values are calculated as an average of 40 indents, and are reported with errors of plus or minus one standard deviation. A fused crystal silica standard is run prior to each test to ensure proper equipment calibration is maintained. Samples are polycrystalline with grain sizes between 10 μm and 30 μm. Prior to indentation testing each sample is vibratory polished using 0.05 μm colloidal silica for 12 h to ensure minimal surface roughness. All tests are carried out at a temperature of 27 °C ± 0.5 °C. Indentation data are analyzed according to the methods of Oliver and Pharr^{69,70}. The elastic (i.e., Young’s) modulus is determined using \(1/E_{{\mathrm{eff}}} = (1  \nu ^2)/E + (1  \nu _{\mathrm{I}}^2)/E_{\mathrm{I}}\), where E_{eff} is the effective modulus (sometimes called the reduced modulus) obtained from nanoindentation, E and ν are the Young’s modulus and Poisson’s ratio, respectively, for the specimen, whereas E_{I} and ν_{I} are the same parameters for the indenter. A Poisson’s ratio for each of the binary carbides is obtained from literature^{71} where available. For fivemetal carbide samples where data for each of the constituents is available, the value used for Poisson’s ratio is taken as the average of the constituent binaries. If this average is unavailable (i.e., when Mo and/or W are present), Poisson’s ratio is assumed to be equal to 0.18.
Data availability
All the abinitio data are freely available to the public as part of the AFLOW online repository and can be accessed through AFLOW.org following the RESTAPI interface^{59} and AFLUX search language^{76}.
References
Gao, M. C, Yeh, J. W, Liaw, P. K. & Zhang, Y. HighEntropy Alloys: Fundamentals and Applications. (Springer, Cham, Switzerland, 2016).
Senkov, O. N., Miller, J. D., Miracle, D. B. & Woodward, C. Accelerated exploration of multiprincipal element alloys with solid solution phases. Nat Commun. 6, 6529 (2015).
Widom, M. Modeling the structure and thermodynamics of highentropy alloys. J. Mater. Res. 33, 2881–2898 (2018).
Lim, X. Mixedup metals make for stronger, tougher, stretchier alloys. Nature 533, 306–307 (2016).
Ye, Y. F., Wang, Q., Lu, J., Liu, C. T. & Yang, Y. Highentropy alloy: challenges and prospects. Mater. Today 19, 349–362 (2016).
Gludovatz, B. et al. A fractureresistant highentropy alloy for cryogenic applications. Science 345, 1153–1158 (2014).
Gali, A. & George, E. P. Tensile properties of high and mediumentropy alloys. Intermetallics 39, 74–78 (2013).
Senkov, O. N., Wilks, G. B., Scott, J. M. & Miracle, D. B. Mechanical properties of Nb_{25}Mo_{25}Ta_{25}W_{25} and V_{20}Nb_{20}Mo_{20}Ta_{20}W_{20} refractory high entropy alloys. Intermetallics 19, 698–706 (2011).
Li, Z., Pradeep, K. G., Deng, Y., Raabe, D. & Tasan, C. C. Metastable highentropy dualphase alloys overcome the strengthductility tradeoff. Nature 534, 227–230 (2016).
Tsao, T.K. et al. The high temperature tensile and creep behaviors of high entropy superalloy. Sci. Rep. 7, 12658 (2017).
Li, Z., Tasan, C. C., Springer, H., Gault, B. & Raabe, D. Interstitial atoms enable joint twinning and transformation induced plasticity in strong and ductile highentropy alloys. Sci. Rep. 7, 40704 (2017).
Senkov, O. N., Wilks, G. B., Miracle, D. B., Chuang, C. P. & Liaw, P. K. Refractory highentropy alloys. Intermetallics 18, 1758–1765 (2010).
Senkov, O. N., Senkova, S. V., Woodward, C. & Miracle, D. B. Lowdensity, refractory multiprincipal element alloys of the CrNbTiVZr system: microstructure and phase analysis. Acta Mater. 61, 1545–1557 (2013).
von Rohr, F., Winiarski, M. J., Tao, J., Klimczuk, T. & Cava, R. J. Effect of electron count and chemical complexity in the TaNbHfZrTi highentropy alloy superconductor. Proc. Natl Acad. Sci. USA 113, E7144–E7150 (2016).
Bérardan, D., Franger, S., Dragoe, D., Meena, A. K. & Dragoe, N. Colossal dielectric constant in high entropy oxides. Phys. Status Solidi RRL 10, 328–333 (2016).
Bérardan, D., Franger, S., Meena, A. K. & Dragoe, N. Room temperature lithium superionic conductivity in high entropy oxides. J. Mater. Chem. A 4, 9536–9541 (2016).
Rost, C. M. et al. Entropystabilized oxides. Nat Commun. 6, 8485 (2015).
Rak, Z. et al. Charge compensation and electrostatic transferability in three entropystabilized oxides: results from density functional theory calculations. J. Appl. Phys. 120, 095105 (2016).
Gild, J. et al. Highentropy metal diborides: a new class of highentropy materials and a new type of ultrahigh temperature ceramics. Sci. Rep. 6, 37946 (2016).
Castle, E., Csanádi, T., Grasso, S., Dusza, J. & Reece, M. Processing and properties of highentropy ultrahigh temperature carbides. Sci. Rep. 8, 8609 (2018).
Dusza, J. et al. Microstructure of (HfTaZrNb)C highentropy carbide at micro and nano/atomic level. J. Eur. Ceram. Soc. 38, 4303–4307 (2018).
Yan, X. et al. (Hf_{0.2}Zr_{0.2}Ta_{0.2}Nb_{0.2}Ti_{0.2})C highentropy ceramics with low thermal conductivity. J. Am. Ceram. Soc. 101, 4486–4491 (2018).
Zhou, J. et al. Highentropy carbide: a novel class of multicomponent ceramics. Ceramics Int. 44, 22014–22018 (2018).
Meisenheimer, P. B., Kratofil, T. J. & Heron, J. T. Giant enhancement of exchange coupling in entropystabilized oxide heterostructures. Sci. Rep. 7, 13344 (2017).
Rohrer, G. S. et al. Challenges in ceramic science: a report from the workshop on emerging research areas in ceramic science. J. Am. Ceram. Soc. 95, 3699–3712 (2012).
Agte, C. & Alterthum, H. Untersuchungen über systeme hochschmelzender carbide nebst beiträgen zum problem der kohlenstoffschmelzung [Investigations of highmelting point carbide systems and their contribution to the problem of carbon fusion]. Z. Tech. Phys. 11, 182–191 (1930).
Andrievskii, R. A., Strel’nikova, N. S., Poltoratskii, N. I., Kharkhardin, E. D. & Smirnov, V. S. Melting point in systems ZrCHfC, TaCZrC, TaCHfC. Poroshk. Metall. 1, 85–88 (1967).
Hong, Q.J. & van de Walle, A. Prediction of the material with highest known melting point from ab initio molecular dynamics calculations. Phys. Rev. B 92, 020104 (2015).
Wuchina, E., Opila, E., Opeka, M., Fahrenholtz, W. & Talmy, I. UHTCs: ultrahigh temperature ceramic materials for extreme environment applications. Electrochem. Soc. Interface 16, 30–36 (2007).
Rudy, E. Ternary Phase Equilibria in Transition MetalBoronCarbonSilicon Systems. Part II. Ternary Systems. (Air Force Materials Laboratory, WrightPatterson Air Force Base, Ohio, USA, 1965).
Rudy, E. Ternary Phase Equilibria in Transition MetalBoronCarbonSilicon Systems. Part V. Compendium of Phase Diagram Data. (Air Force Materials Laboratory, WrightPatterson Air Force Base, Ohio, USA, 1969).
Gusev, A. I. Phase diagrams of the pseudobinary TiCNbC, TiCTaC, ZrCNbC, ZrCTaC, and HfCTaC carbide systems. Russ. J. Phys. Chem. 59, 336–340 (1985).
CedillosBarraza, O. et al. Sintering behaviour, solid solution formation and characterisation of TaC, HfC and TaCHfC fabricated by spark plasma sintering. J. Eur. Ceram. Soc. 36, 1539–1548 (2016).
CedillosBarraza, O. et al. Investigating the highest melting temperature materials: a laser melting study of the TaCHfC system. Sci. Rep. 6, 37962 (2016).
Landau, D. P., Tsai, S.H. & Exler, M. A new approach to Monte Carlo simulations in statistical physics: WangLandau sampling. Am. J. Phys. 72, 1294–1302 (2004).
Baldock, R. J. N., Pártay, L. B., Bartók, A. P., Payne, M. C. & Csányi, G. Determining pressuretemperature phase diagrams of materials. Phys. Rev. B 93, 174108 (2016).
Gao, M. C. & Alman, D. E. Searching for next singlephase highentropy alloy compositions. Entropy 15, 4504–4519 (2013).
Zhang, F. et al. An understanding of high entropy alloys from phase diagram calculations. Calphad 45, 1–10 (2014).
Gao, M. C. et al. Thermodynamics of concentrated solid solution alloys. Curr. Opin. Solid State Mater. Sci. 21, 238–251 (2017).
Curtarolo, S. et al. The highthroughput highway to computational materials design. Nat. Mater. 12, 191–201 (2013).
Yang, K., Oses, C. & Curtarolo, S. Modeling offstoichiometry materials with a highthroughput abinitio approach. Chem. Mater. 28, 6484–6492 (2016).
Bergerhoff, G., Hundt, R., Sievers, R. & Brown, I. D. The inorganic crystal structure data base. J. Chem. Inf. Comput. Sci. 23, 66–69 (1983).
Massalski, T. B., Okamoto, H., Subramanian, P. R. & Kacprzak, L. Binary Alloy Phase Diagrams. (ASM International, Materials Park, Ohio, USA, 1990).
Oses, C. et al. AFLOWCHULL: cloudoriented platform for autonomous phase stability analysis. J. Chem. Inf. Model. https://doi.org/10.1021/acs.jcim.8b00393 (2018).
Lederer, Y., Toher, C., Vecchio, K. S. & Curtarolo, S. The search for high entropy alloys: a highthroughput abinitio approach. Acta Mater. 159, 364–383 (2018).
Perim, E. et al. Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases. Nat. Commun. 7, 12315 (2016).
Greer, A. L. Confusion by design. Nature 366, 303–304 (1993).
Kuo, K. & Hägg, G. A new molybdenum carbide. Nature 170, 245–246 (1952).
Williamson, G. K. & Hall, W. H. Xray line broadening from filed aluminium and wolfram. Acta Metall. 1, 22–31 (1953).
Freudenberger, J. et al. Face centred cubic multicomponent equiatomic solid solutions in the AuCuNiPdPt system. Metals 7, 135 (2017).
Curtarolo, S. et al. AFLOWLIB.ORG: a distributed materials properties repository from highthroughput ab initio calculations. Comput. Mater. Sci. 58, 227–235 (2012).
Smith, C. J., Yu, X.X., Guo, Q., Weinberger, C. R. & Thompson, G. B. Phase, hardness, and deformation slip behavior in mixed Hf_{x}Ta_{1−x}C. Acta Mater. 145, 142–153 (2018).
Toher, C. et al. Combining the AFLOW GIBBS and elastic libraries to efficiently and robustly screen thermomechanical properties of solids. Phys. Rev. Mater. 1, 015401 (2017).
Chen, X.Q., Niu, H., Li, D. & Li, Y. Modeling hardness of polycrystalline materials and bulk metallic glasses. Intermetallics 19, 1275–1281 (2011).
Teter, D. M. Computational alchemy: the search for new superhard materials. MRS Bull. 23, 22–27 (1998).
Tian, Y., Xu, B. & Zhao, Z. Microscopic theory of hardness and design of novel superhard crystals. Int. J. Refract. Met. Hard Mater. 33, 93–106 (2012).
Toher, C. et al. Highthroughput computational screening of thermal conductivity, Debye temperature, and Grüneisen parameter using a quasiharmonic Debye model. Phys. Rev. B 90, 174107 (2014).
van de Walle, A. & Ceder, G. The effect of lattice vibrations on substitutional alloy thermodynamics. Rev. Mod. Phys. 74, 11–45 (2002).
Curtarolo, S. et al. AFLOW: an automatic framework for highthroughput materials discovery. Comput. Mater. Sci. 58, 218–226 (2012).
Calderon, C. E. et al. The AFLOW standard for highthroughput materials science calculations. Comput. Mater. Sci. 108 Part A, 233–238 (2015).
Rappe, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A. III & Skiff, W. M. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 114, 10024–10035 (1992).
Mehl, M. J. et al. The AFLOW library of crystallographic prototypes: part 1. Comput. Mater. Sci. 136, S1–S828 (2017).
Kresse, G. & Furthmüller, J. Efficient iterative schemes for ab initio totalenergy calculations using a planewave basis set. Phys. Rev. B 54, 11169–11186 (1996).
Perdew, J. P., Burke, K. & Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865–3868 (1996).
Suh, N. P. & Turner, A. P. L. Elements of the Mechanical Behavior of Solids (McGrawHill, New York, 1975).
Materials Data, Inc. Jade 9. https://materialsdata.com/prodjd.html (Materials Data Inc., 2016).
Cullity, B. D. Elements of XRay Diffraction. (AddisonWesley, Reading, MA, USA, 1956).
Mote, V. D., Purushotham, Y. & Dole, B. N. WilliamsonHall analysis in estimation of lattice strain in nanometersized ZnO particles. J. Theor. Appl. Phys. 6, 6 (2012).
Oliver, W. C. & Pharr, G. M. An improved technique for determining hardness and elastic modulus using load and displacement sensing indentation experiments. J. Mater. Res. 7, 1564–1583 (1992).
Oliver, W. C. & Pharr, G. M. Measurement of hardness and elastic modulus by instrumented indentation: advances in understanding and refinements to methodology. J. Mater. Res. 19, 3–20 (2004).
Kral, C., Lengauer, W., Rafaja, D. & Ettmayer, P. Critical review on the elastic properties of transition metal carbides, nitrides and carbonitrides. J. Alloy. Compd. 265, 215–233 (1998).
American Society for Metals. ASM Engineered Materials Reference Book (ASM International, Materials Park, Ohio, USA, 1989).
Oyama, S. T. (ed.) The Chemistry of Transition Metal Carbides and Nitrides (Blackie Academic & Professional, Glasgow, UK, 1996).
Dubitsky, G. A. et al. Superhard superconductor composites obtained by sintering of diamond, cBN and C_{60} powders with superconductors. Z. Naturforsch. B 61, 1541–1546 (2006).
Caron, P. & Tremblay, A. Method for treating tungsten carbide particles. US patent 7981394 B2 (2011).
Rose, F. et al. AFLUX: The LUX materials search API for the AFLOW data repositories. Comput. Mater. Sci. 137, 362–370 (2017).
Acknowledgements
The authors acknowledge support by DODONR (N000141512863, N000141712090, N000141612583, N000141712876) and by Duke University—Center for Materials Genomics—for computational support. S.C. acknowledges the Alexander von Humboldt Foundation for financial support. C.O. acknowledges support from the National Science Foundation Graduate Research Fellowship under grant no. DGF1106401. The authors thank Axel van de Walle, Matthias Scheffler, Claudia Draxl, Ohad Levy, Yoav Lederer, Amir Natan, Omar Cedillos Barraza, Joshua Gild, Olivia Dippo, and Cameron McElfresh for helpful discussions.
Author information
Authors and Affiliations
Contributions
S.C. proposed the entropy spectral descriptor and the phase stabilization mechanism. C.T. wrote the AEL–AGL codes under the supervision of S.C. C.O. wrote the AFLOWPOCC and AFLOWCHULL codes under the supervision of C.T. and S.C. P.S. and C.T. performed the AFLOWPOCC and AEL–AGL calculations for the singlephase systems. C.O. performed the convex hull analysis. T.H. made the samples under the supervision of K.V. T.H. and M.S. performed the experimental characterization. All authors—P.S., T.H., C.T., C.O., M.S., J.P.M., D.B., K.V., S.C.—discussed the results and contributed to the writing of the article.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Sarker, P., Harrington, T., Toher, C. et al. Highentropy highhardness metal carbides discovered by entropy descriptors. Nat Commun 9, 4980 (2018). https://doi.org/10.1038/s41467018071607
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467018071607
This article is cited by

Machine learningdriven synthesis of TiZrNbHfTaC5 highentropy carbide
npj Computational Materials (2023)

Functional twodimensional highentropy materials
Communications Materials (2023)

Phase formation capability and compositional design of βphase multiple rareearth principal component disilicates
Nature Communications (2023)

Firstprinciple study on the stability of Cd passivates in soil
Scientific Reports (2023)

Design highentropy carbide ceramics from machine learning
npj Computational Materials (2022)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.