Computational materials discovery is a booming field of science, which helps in predicting new unexpected materials with optimal combinations of various physical properties. Going beyond the targeted search for new materials within prespecified systems, the recently developed method, Mendelevian search, allows one to look for materials with the desired properties across the entire Periodic Table, indicating possibly superhard (or other) materials that could be obtained experimentally. From this viewpoint, we discuss the recently developed methods for crystal structure prediction and empirical models of Vickers hardness and fracture toughness that allow fast screening for materials with optimal mechanical properties. We also discuss the results of the computational search for hard and superhard materials obtained in the last few years using these novel approaches and present a “treasure map” of hard and superhard materials, which summarizes known and predicted materials and points to promising future directions of superhard materials discovery.

Superhard materials are a class of materials with unique mechanical properties that are widely used in many industrial applications, like mining, defense, and space industries. A material can be called superhard if its Vickers hardness is greater than 40 GPa.1–3 The hardest material known to date is diamond, with Vickers hardness in the range of 60–120 GPa.4,5 Some metastable carbon allotropes also have a high Vickers hardness,6–9 but none of them surpass diamond. Among the noncarbon superhard materials, cubic boron nitride displays the high Vickers hardness of ∼60 GPa.1,2,10,11 Materials with unique mechanical properties include some borides, carbides, and nitrides of transition metals, such as chromium,12–14 rhenium,15–17 molybdenum,18,19 tungsten,20–27 etc. Some of these carbides (WC) and nitrides (TiN) are already widely used in manufacturing and mining, e.g., in drilling equipment.

Diamond (as well as other sp3 allotropes of carbon) and cubic BN are high-pressure phases. In this work, we pay more attention to hard and superhard transition metal borides, carbides, and nitrides, many of which are stable at ambient conditions and easy to synthesize. We also cover recent advances in the prediction of new materials that may find a variety of applications. Crystal structures of some hard and superhard materials are shown in Fig. 1.

FIG. 1.

Crystal structures of the most used and well-known hard and superhard materials, including c-BN, WC, TiN, WB5,26 α-B, diamond, TiB2, γ-B28,28 and CrB4.

FIG. 1.

Crystal structures of the most used and well-known hard and superhard materials, including c-BN, WC, TiN, WB5,26 α-B, diamond, TiB2, γ-B28,28 and CrB4.

Close modal

Titanium nitride's Vickers hardness of 22 GPa,29 though not extraordinary, makes it suitable for various applications. Tungsten carbide (WC) has a higher Vickers hardness of ∼30 GPa,30,31 enabling its use in “hard alloys” (WC-based composites) that are actively used in drilling, metal- and woodworking (in drawing tools, drill bits, cutters), etc. The composite of tungsten carbide and cobalt with WC:Co = 9:1 ratio is used for making metal cutting and drilling tools since 1930s. This hard alloy's manufacturing process is relatively simple, making it possible to easily scale its production: A fine powder of tungsten carbide or other refractory carbide and a fine powder of cobalt or nickel metal binder are mixed, pressed into molds, and sintered at a temperature close to the melting point of the binder metal, producing a very dense and hard material.

However, many transition metal borides and carbides pose challenges. From the experimental point of view, it is extremely difficult to determine the correct composition and crystal structure of transition metal borides and carbides by X-ray diffraction methods. This leads to errors in determining the mechanical properties of synthesized samples of an unclarified compound. Examples include the ongoing controversy around the exact composition and crystal structure of the highest tungsten boride and the superhardness of rhenium diboride, reported to have a hardness of 48 GPa17 which was later refuted,32 in agreement with our theoretical calculations (see below).

Crystal structure prediction methods develop rapidly33–35 and can already be used to discover novel superhard materials. To facilitate this, simple and reliable ways of computing hardness and fracture toughness are necessary.

Several developed empirical models,36–39 which require as an input the crystal structure and chemical composition or the elastic properties, can be used to calculate the hardness of different materials. One of the models proposed by Chen et al.38 is based on the assumption that the indentation size is correlated with the shear modulus of the material (G), while the width of a formed imprint is proportional to the bulk modulus (B),38 

HvG(G/B)2.
(1)

Analysis of experimental data on many materials38 has led to the following empirical formula for Vickers hardness (HV):

HV=2(k2G)0.5853,
(2)

where k is the Pugh ratio (k = G/B), G is the shear modulus, and B is the bulk modulus; Hv, B, and G are expressed in GPa. Calculations of Vickers hardness for a number of materials using Chen's model agree well with experimental data:38 the calculated value for diamond is 98 (experimental value ∼964,40), for TiN—22.6 (20.529), and for c-BN—56.9 (∼551,41). However, this reliable model requires calculations of the elastic constants.

The Lyakhov-Oganov model39 is more convenient for high-throughput searches, numerically stable, usually reliable and can be successfully used for fast Pareto-screening for superhard materials. This model requires only the crystal structure and chemical composition; the hardness is computed from individual bond hardnesses.

It is important to compare the accuracy of both empirical models with the available theoretical and experimental data. The comparison of the results obtained by using these models of hardness with the experimental data that we calculated for various predicted stable and metastable materials is shown in Fig. 2. Here, the largest studied class of materials is transition metal borides (TMBs). The two models are consistent with each other and with the reference experimental data for TMBs. Theoretical hardnesses are more often underestimated than overestimated, compared with the experimental data (Fig. 2). One of the exceptions is WB4, which may be related to the incorrectly determined structure and/or composition of the synthesized material. For transition metal polynitrides with quasimolecular Nn groups (Cr-N and Mo-N systems, the blue region in Fig. 2), there is a significant difference between these models, showing that Chen's model38 is more accurate. This is because the Lyakhov-Oganov model includes very strong N–N bonds in the calculation of hardness, while only metal-nitrogen bonds are broken during hardness tests. Expectedly, the Lyakhov-Oganov model overestimates the hardness of such polynitrides.

FIG. 2.

Comparison of the computed hardness by Chen (blue bars) and Lyakhov-Oganov (red bars) with the available reference data from Refs. 1, 4, 12, 13, 21, 25, 29, and 41–56. Reference data with error bars are experimental; those without error bars are theoretical.

FIG. 2.

Comparison of the computed hardness by Chen (blue bars) and Lyakhov-Oganov (red bars) with the available reference data from Refs. 1, 4, 12, 13, 21, 25, 29, and 41–56. Reference data with error bars are experimental; those without error bars are theoretical.

Close modal

For a very large number of materials, including diamond, c-BN, SiC, borides, hydrides, etc., a much better agreement can be observed between both theoretical models and the experimental data. Among experimental data, an unusual situation can be noted for γ-B28; recent experimental measurement of low Vickers hardness of γ-B2857 (∼30 GPa) looks controversial: there are two earlier measurements of the hardness of γ-B28,28,58 indicating that it is superhard (Hv = 50–58 GPa). It is possible that the low value of hardness for γ-B28 is due to systematic underestimation: in the same set of experiments, Vickers hardness of cubic BN was also underestimated (47 GPa vs ∼60 GPa from Refs. 1, 2, 10, and 11). Similar values (∼50 GPa) of hardness of γ-B28 result from physically motivated empirical models; such models (which can be either analytical or based on machine learning) are justified, and one can see how well this model describes the hardness of very different materials (Fig. 2).

For industrial applications, fracture toughness, along with hardness, also plays a key role. Unfortunately, the hardest materials are usually brittle (covalent crystals), while the materials with the highest fracture toughness are metals whose hardness ranges from low to medium.

Fracture toughness can be calculated using the empirical model from Ref. 59,

KIC=αV16G(BG)12,
(3)

where α is the enhancement factor accounting for the degree of metallicity, V is the volume per atom, and G and B are the shear and bulk moduli, respectively.59 For insulators, semiconductors, transition metal carbides, nitrides, borides, and hydrides, α = 1.59 The calculated values of fracture toughness of diamond, WC, TiN, and c-BN are close to those measured experimentally, e.g., 6.33 MPa m0.5 (experiment: 4–7 MPa m0.5)60–62 for diamond, 5.37 MPa m0.5 (experiment: 5–8 MPa m0.5)30,63 for WC, 3.9 MPa m0.5 (experiment: 3.4–5 MPa m0.5)63 for TiN, and 5.41 MPa m0.5 (experiment: 2–5 MPa m0.5)41,62 for c-BN.

Chromium-based materials reveal attractive mechanical properties. In the Cr-C system, only three stable carbides, Cr23C6, Cr3C2, and Cr7C3,64–68 are known from experiments. Two metastable chromium carbides, CrC and Cr3C, have also been synthesized.69–72 In the Cr-B system, six different phases, Cr2B, Cr5B3, CrB, Cr3B4, CrB2, and CrB4, are known from experiments,12,50,73–76 and their mechanical properties were examined theoretically.13,14,50,77 A recent theoretical study of chromium carbides14 using the evolutionary algorithm USPEX78–80 has led to the prediction of new stable phase Pmn2-Cr2C, in addition to already known phases.65,81Pmn2-Cr2C phase is anticipated to have the highest shear modulus of 292 GPa among all chromium carbides and the Vickers hardness of 27 GPa.14 The highest Vickers hardness among chromium-based materials is achieved in CrB4 and was reported to be in the range of 29–44 GPa,14,49 while our calculations give 48 GPa.

Tungsten borides are extremely interesting phases, displaying mechanical properties suitable for industrial applications. One of the first reports on their synthesis was made by Kiessling82 in 1947. In 1963, the first phase diagram of the W-B system was drawn.83 Since then, the W-B phase diagram was reinvestigated and clarified several times.84–87 Five stable tungsten borides were reported in experiments: W2B,20,82 WB (α and β phases),44,82 WB2,88 and the much debated WB4.20,21,23 Numerous theoretical studies of the stability and properties of these phases were published recently.15,22,25,26,89,90 Wide homogeneity regions of W-B phases, mentioned in theoretical and experimental works,20,25,91 may at least partly be caused by an extensive polysomatism.92 This often leads to difficulties in the synthesis of stoichiometric phases and to inaccurate crystallographic descriptions of synthesized phases by experimental methods, especially in determining the exact positions of light boron atoms using X-ray diffraction.

Because of this, the phase originally claimed in 1947 as W2B582 was correctly identified as W2B4 with the P63/mmc space group only after 60 years.91,93 Another example of contradiction is boron-rich phases observed for the first time by Chretien and Helcorsky,94 where the composition was initially reported to be WB4,94 while other works presented it as W2B9,95 WB12,96 and W1−xB3.86 Various experimental techniques were used to determine and characterize the obtained samples.95,97 This phase with unknown composition and crystal structure has made the W-B system very popular in recent studies, with Gu et al.21 suggesting that WB4 may be superhard. Its hardness was measured experimentally and calculated theoretically. It was determined that its Vickers hardness is higher than 40 GPa23,98 and can even reach 57.3 ± 1.9 GPa (under the load of 0.49 N) with the addition of chromium.99 Later, Cheng et al.100 showed that this material's composition is WB3 + x, not WB4. A large excess of boron can provide good crystallinity and stability of WB3 + x.20 

A recent evolutionary computational search26 for new stable superhard tungsten borides shows that in the boron-rich region of the W-B phase diagram, a new Pmmn-WB5 phase is stable at ambient conditions. WB4 was predicted to be thermodynamically stable only at pressures above 1 GPa.27 The most intriguing properties of the newly predicted WB5 are a combination of very high Vickers hardness, ∼45 GPa, and high fracture toughness, 4 MPa m0.5. In addition, it is thermodynamically stable at ambient pressure and predicted to retain excellent mechanical properties even at 2000 K. According to these predictions, WB5 is a promising material for practical applications.

Modern technology requires new materials displaying unique combinations of mechanical, electronic, and other properties to replace traditional materials widely used in the industry. Recently, we developed a new method101 that allows one to predict optimal materials from among all possible combinations of all the elements across the Periodic Table. This nonempirical method combines our coevolutionary approach with a carefully restructured “Mendelevian” chemical space, energy filtering, and Pareto optimization102 of target properties and stability, to ensure that the predicted materials have attractive properties and a high chance to be synthesizable. Variation operators (i.e., the “moves” in the structural and compositional space) are of central importance in every evolutionary/coevolutionary algorithm and need to be designed carefully. For this reason, the variation operators of this method work in the space of two important elemental properties (electronegativity and atomic radius) and are designed in a way that newly sampled compounds are based on elements that are similar in their properties to elements present in already sampled compounds with good properties. This definition of chemical space originates from Goldschmidt's law103,104 of crystal chemistry (which states that crystal structure is determined by stoichiometry, atomic size, electronegativity, and polarizability of atoms/ions) and was inspired by the Mendeleev number of Pettifor105,106 (for more details on our new method, Mendelevian search, or MendS, see Ref. 101). This way, step by step, the algorithm learns about promising regions of the chemical space and focuses on them at the expense of unpromising regions. Using our MendS method, we carefully studied the chemical space constructed from 74 elements (all the elements, excluding the noble gases, rare-earth elements, and elements heavier than Pu) and searched for materials with optimal hardness (calculated by the Lyakhov-Oganov empirical model39) and stability (computed using Maxwell's convex hull construction). As Chen's model38 is more accurate, it was used to obtain the final theoretical hardness, which was then used for constructing the Ashby plot of the Vickers hardness vs fracture toughness (Fig. 3). This results in a map on which materials with an optimal combination of the Vickers hardness and fracture toughness can be easily found.

FIG. 3.

Ashby plot of Vickers hardness vs fracture toughness for various materials. Phases marked by a red point are thermodynamically stable at ambient conditions. Structures of B4CO4, two high-pressure phases of B2O3 (“hardest oxides”), and γ-B were taken from Refs. 28, 107, and 108.

FIG. 3.

Ashby plot of Vickers hardness vs fracture toughness for various materials. Phases marked by a red point are thermodynamically stable at ambient conditions. Structures of B4CO4, two high-pressure phases of B2O3 (“hardest oxides”), and γ-B were taken from Refs. 28, 107, and 108.

Close modal

The multiobjective Pareto technique102 and energy filtering (discarding the structures with energy above the convex hull by more than 0.5 eV/atom) were used to ensure that the evolutionary algorithm generates hard phases that have low energy. In this calculation, 600 binary systems were studied in 20 MendS generations (which is only about one-fifth of all the systems that can be made of 74 elements). Impressively, all the hard binary materials reported in the literature and materials claimed to be potentially hard were found in our calculation. Diamond (and its polytype lonsdaleite) as the hardest, and boron allotropes as the second hardest elemental phases, were found correctly in our Mendelevian search. BxCy,109–111 BxOy,108–112 CxNy,113,114 BxNy,1,2,10,11 CrxCy,53,54,115 CrxBy,13,48–50,77 CrxNy,53,54,115 RexBy,15–17,32 WxBy,20–27 SixCy,36,37,116,117 WxCy,36,37,118 AlxOy,36,117 TixCy,119 SixNy,118 TixNy,118 BexOy,118 RuxOy,119–121 OsxOy,122 RhxBy,123 IrxBy,123 OsxBy,124–126 and RuxBy124–126 are some of the examples of binary systems reported to be hard in the literature and found by us in this single calculation. Moreover, our calculation revealed several binary systems with very promising hardness: MoxBy,18,19 BxPy,127 VxBy,128–130 FexBy,131,132 MnxBy,133,134 and MnxHy.

In the W-B system, the most promising material is WB5, predicted26 to be thermodynamically stable and to have high hardness and fracture toughness. The Mn-H system appeared in our list unexpectedly; it was never thought to contain hard phases. However, we showed that many phases in this system are stable or low-energy metastable, have high-symmetry structures, and hard indeed, having the Vickers hardness in the range of 20–30 GPa (Fig. 3). Figure 3, the Ashby plot of Vickers hardness and fracture toughness, is a “treasure map” of superhard materials, summarizing much of what we know and expect today and pointing to future directions of research. Concerning low-pressure phases, the best combination of hardness and fracture toughness is achieved in TiB2, ZrB2, VB, V3B4, VB2, VB12, CrB4, WB5, MnB4, ReB2, and WC.

We have discussed here several important examples of transition metal boride systems that were computationally predicted to be promising for practical applications. The new method, Mendelevian search, shows great predictive power in the search for hard and superhard materials. Modern computational techniques allow the prediction and relatively fast development of new materials (e.g., superhard materials) with enhanced properties, destined to replace traditional materials.

A.R.O. was supported by the Russian Science Foundation (Grant No. 19-72-30043). A.G.K. thanks the Russian Science Foundation (Grant No. 17-73-20038). This work was carried out using our Rurik supercomputer at MIPT and the Arkuda supercomputer of the Skolkovo Foundation.

1.
V. L.
Solozhenko
,
S. N.
Dub
, and
N. V.
Novikov
,
Diamond Relat. Mater.
10
,
2228
(
2001
).
2.
V. L.
Solozhenko
and
E.
Gregoryanz
,
Mater. Today
8
,
44
(
2005
).
3.
V. L.
Solozhenko
,
O. O.
Kurakevych
,
D.
Andrault
,
Y.
Le Godec
, and
M.
Mezouar
,
Phys. Rev. Lett.
102
,
015506
(
2009
).
4.
R. A.
Andrievski
,
Int. J. Refract. Metals Hard Mater.
19
,
447
(
2001
).
5.
6.
V. D.
Blank
,
S. G.
Buga
,
N. R.
Serebryanaya
,
G. A.
Dubitsky
,
B. N.
Mavrin
,
M. Yu.
Popov
,
R. H.
Bagramov
,
V. M.
Prokhorov
,
S. N.
Sulyanov
,
B. A.
Kulnitskiy
, and
Ye. V.
Tatyanin
,
Carbon
36
,
665
(
1998
).
7.
Q.
Li
,
Y.
Ma
,
A. R.
Oganov
,
H.
Wang
,
H.
Wang
,
Y.
Xu
,
T.
Cui
,
H.-K.
Mao
, and
G.
Zou
,
Phys. Rev. Lett.
102
,
175506
(
2009
).
8.
C.
He
,
L.
Sun
,
C.
Zhang
,
X.
Peng
,
K.
Zhang
, and
J.
Zhong
,
Solid State Commun.
152
,
1560
(
2012
).
9.
W. L.
Mao
,
H.
Mao
,
P. J.
Eng
,
T. P.
Trainor
,
M.
Newville
,
C.
Kao
,
D. L.
Heinz
,
J.
Shu
,
Y.
Meng
, and
R. J.
Hemley
,
Science
302
,
425
(
2003
).
10.
Y.
Li
,
J.
Hao
,
H.
Liu
,
S.
Lu
, and
J. S.
Tse
,
Phys. Rev. Lett.
115
,
105502
(
2015
).
11.
C.
He
,
L.
Sun
,
C.
Zhang
,
X.
Peng
,
K.
Zhang
, and
J.
Zhong
,
Phys. Chem. Chem. Phys.
14
,
10967
(
2012
).
12.
L.
Han
,
S.
Wang
,
J.
Zhu
,
S.
Han
,
W.
Li
,
B.
Chen
,
X.
Wang
,
X.
Yu
,
B.
Liu
,
R.
Zhang
,
Y.
Long
,
J.
Cheng
,
J.
Zhang
,
Y.
Zhao
, and
C.
Jin
,
Appl. Phys. Lett.
106
,
221902
(
2015
).
13.
M.-M.
Zhong
,
C.
Huang
, and
C.-L.
Tian
,
Int. J. Mod. Phys. B
30
,
1650201
(
2016
).
14.
A. G.
Kvashnin
,
A. R.
Oganov
,
A. I.
Samtsevich
, and
Z.
Allahyari
,
J. Phys. Chem. Lett.
8
,
755
(
2017
).
15.
M.-M.
Zhong
,
X.-Y.
Kuang
,
Z.-H.
Wang
,
P.
Shao
,
L.-P.
Ding
, and
X.-F.
Huang
,
J. Phys. Chem. C
117
,
10643
(
2013
).
16.
A.
Latini
,
J. V.
Rau
,
D.
Ferro
,
R.
Teghil
,
V. R.
Albertini
, and
S. M.
Barinov
,
Chem. Mater.
20
,
4507
(
2008
).
17.
H.-Y.
Chung
,
M. B.
Weinberger
,
J. B.
Levine
,
A.
Kavner
,
J.-M.
Yang
,
S. H.
Tolbert
, and
R. B.
Kaner
,
Science
316
,
436
(
2007
).
18.
M.
Zhang
,
H.
Wang
,
H.
Wang
,
T.
Cui
, and
Y.
Ma
,
J. Phys. Chem. C
114
,
6722
(
2010
).
19.
Y.
Liang
,
X.
Yuan
,
Z.
Fu
,
Y.
Li
, and
Z.
Zhong
,
Appl. Phys. Lett.
101
,
181908
(
2012
).
20.
H.
Itoh
,
T.
Matsudaira
,
S.
Naka
,
H.
Hamamoto
, and
M.
Obayashi
,
J. Mater. Sci.
22
,
2811
(
1987
).
21.
Q.
Gu
,
G.
Krauss
, and
W.
Steurer
,
Adv. Mater.
20
,
3620
(
2008
).
22.
E.
Zhao
,
J.
Meng
,
Y.
Ma
, and
Z.
Wu
,
Phys. Chem. Chem. Phys.
12
,
13158
(
2010
).
23.
R.
Mohammadi
,
A. T.
Lech
,
M.
Xie
,
B. E.
Weaver
,
M. T.
Yeung
,
S. H.
Tolbert
, and
R. B.
Kaner
,
Proc. Natl. Acad. Sci. U.S.A.
108
,
10958
(
2011
).
24.
S.
Aydin
,
Y. O.
Ciftci
, and
A.
Tatar
,
J. Mater. Res.
27
,
1705
(
2012
).
25.
X.-Y.
Cheng
,
X.-Q.
Chen
,
D.-Z.
Li
, and
Y.-Y.
Li
,
Acta Crystallogr. C
70
,
85
(
2014
).
26.
A. G.
Kvashnin
,
H. A.
Zakaryan
,
C.
Zhao
,
Y.
Duan
,
Y. A.
Kvashnina
,
C.
Xie
,
H.
Dong
, and
A. R.
Oganov
,
J. Phys. Chem. Lett.
9
,
3470
(
2018
).
27.
C.
Zhao
,
Y.
Duan
,
J.
Gao
,
W.
Liu
,
H.
Dong
,
H.
Dong
,
D.
Zhang
, and
A. R.
Oganov
,
Phys. Chem. Chem. Phys.
20
,
24665
(
2018
).
28.
A. R.
Oganov
,
J.
Chen
,
C.
Gatti
,
Y.
Ma
,
Y.
Ma
,
C. W.
Glass
,
Z.
Liu
,
T.
Yu
,
O. O.
Kurakevych
, and
V. L.
Solozhenko
,
Nature
457
,
863
(
2009
).
29.
D. S.
Stone
,
K. B.
Yoder
, and
W. D.
Sproul
,
J. Vac. Sci. Technol. A
9
,
2543
(
1991
).
30.
M. P.
Groover
,
Fundamentals of Modern Manufacturing: Materials, Processes, and Systems
(
John Wiley & Sons
,
2010
).
31.
V. N.
Chuvildeev
,
Yu. V.
Blagoveshchensky
,
M. S.
Boldin
,
N. V.
Sakharov
,
A. V.
Nokhrin
,
N. V.
Isaeva
,
S. V.
Shotin
,
Yu. G.
Lopatin
,
O. A.
Belki
, and
E. S.
Smirnova
,
Tech. Phys. Lett.
41
,
397
(
2015
).
32.
N.
Dubrovinskaia
,
L.
Dubrovinsky
, and
V. L.
Solozhenko
,
Science
318
,
1550
(
2007
).
33.
A. R.
Oganov
,
Modern Methods of Crystal Structure Prediction
(
Wiley
,
2011
).
34.
A. R.
Oganov
,
G.
Saleh
, and
A. G.
Kvashnin
,
Computational Materials Discovery
(
Royal Society of Chemistry
,
2018
).
35.
A. R.
Oganov
,
C. J.
Pickard
,
Q.
Zhu
, and
R. J.
Needs
,
Nature Rev. Mater.
4
,
331
(
2019
).
36.
F.
Gao
,
J.
He
,
E.
Wu
,
S.
Liu
,
D.
Yu
,
D.
Li
,
S.
Zhang
, and
Y.
Tian
,
Phys. Rev. Lett.
91
,
015502
(
2003
).
37.
A.
Simunek
and
J.
Vackar
,
Phys. Rev. Lett.
96
,
85501
(
2006
).
38.
X.-Q.
Chen
,
H.
Niu
,
D.
Li
, and
Y.
Li
,
Intermetallics
19
,
1275
(
2011
).
39.
A. O.
Lyakhov
and
A. R.
Oganov
,
Phys. Rev. B
84
,
092103
(
2011
).
40.
M.
Popov
,
B.
Kulnitskiy
, and
V.
Blank
,
Comprehensive Hard Materials
(
Elsevier
,
Oxford
,
2014
), pp.
515
538
.
41.
V. L.
Solozhenko
,
O. O.
Kurakevych
, and
Y.
Le Godec
,
Adv. Mater.
24
,
1540
(
2012
).
42.
H.
Niu
,
S.
Niu
, and
A. R.
Oganov
,
J. Appl. Phys.
125
, 065105 (
2019
).
43.
S.
Okada
,
T.
Atoda
,
I.
Higashi
, and
Y.
Takahashi
,
J. Mater. Sci.
22
,
2993
(
1987
).
44.
S.
Okada
,
K.
Kudou
, and
T.
Lundström
,
Jpn. J. Appl. Phys.
34
,
226
(
1995
).
45.
Y.
Liang
,
Z.
Zhong
, and
W.
Zhang
,
Comput. Mater. Sci.
68
,
222
(
2013
).
46.
Y.
Liang
,
Z.
Fu
,
X.
Yuan
,
S.
Wang
,
Z.
Zhong
, and
W.
Zhang
,
Europhys. Lett.
98
,
66004
(
2012
).
47.
Landolt-Börnstein: Numerical Data and Functional Relationships in Science and Technology—New Series (
Springer
,
2004
).
48.
S.
Okada
,
T.
Shishido
,
K.
Yubuta
, and
T.
Mori
,
Pac. Sci. Rev.
14
,
97
(
2012
).
49.
S.
Wang
,
X.
Yu
,
J.
Zhang
,
Y.
Zhang
,
L.
Wang
,
K.
Leinenweber
,
H.
Xu
,
D.
Popov
,
C.
Park
,
W.
Yang
,
D.
He
, and
Y.
Zhao
,
J. Superhard Mater.
36
,
279
(
2014
).
50.
H.
Niu
,
J.
Wang
,
X.-Q.
Chen
,
D.
Li
,
Y.
Li
,
P.
Lazar
,
R.
Podloucky
, and
A. N.
Kolmogorov
,
Phys. Rev. B
85
,
144116
(
2012
).
51.
Z.
Zhao
,
K.
Bao
,
F.
Tian
,
D.
Duan
,
B.
Liu
, and
T.
Cui
,
Phys. Rev. B
93
,
214104
(
2016
).
52.
Y.
Li
,
Y.
Gao
,
B.
Xiao
,
T.
Min
,
Y.
Yang
,
S.
Ma
, and
D.
Yi
,
J. Alloys Compd.
509
,
5242
(
2011
).
53.
H.
Motono
,
M.
Yoshinaka
,
K.
Hirota
, and
O.
Yamaguchi
,
J. Jpn. Soc. Powder Powder Metall.
50
,
372
(
2003
).
54.
J.
Esteve
,
J.
Romero
,
M.
Gómez
, and
A.
Lousa
,
Surf. Coat. Technol.
188–189
,
506
(
2004
).
55.
C.
Jiang
,
Appl. Phys. Lett.
92
,
041909
(
2008
).
56.
K.
Hirota
,
K.
Mitani
,
M.
Yoshinaka
, and
O.
Yamaguchi
,
Mater. Sci. Eng. A
399
,
154
(
2005
).
57.
S. H.
Zhang
,
X.
Zheng
,
Q. Q.
Jin
,
S. J.
Zheng
,
D.
Legut
,
X. H.
Yu
,
H. Y.
Gou
,
Z. H.
Fu
,
Y. Q.
Guo
,
B. M.
Yan
,
C.
Peng
,
C. Q.
Jin
,
T. C.
Germann
, and
R. F.
Zhang
,
Phys. Rev. Mater.
2
,
123602
(
2018
).
58.
V. L.
Solozhenko
,
O. O.
Kurakevych
, and
A. R.
Oganov
,
J. Superhard Mater.
30
,
428
(
2008
).
59.
H.
Niu
,
S.
Niu
, and
A. R.
Oganov
,
J. Appl. Phys.
125
,
065105
(
2019
).
60.
M. D.
Drory
,
R. H.
Dauskardt
,
A.
Kant
, and
R. O.
Ritchie
,
J. Appl. Phys.
78
,
3083
(
1995
).
61.
N.
Dubrovinskaia
,
S.
Dub
, and
L.
Dubrovinsky
,
Nano Lett.
6
,
824
(
2006
).
62.
C. A.
Brookes
,
Inst. Phys. Conf. Ser.
75
,
207
(
1986
).
63.
R. G.
Munro
,
S. W.
Freiman
, and
T. L.
Baker
,
Fracture Toughness Data for Brittle Materials
(
U.S. Department of Commerce, National Institute of Standards and Technology
,
Gaithersburg
,
MD
,
1998
).
64.
M.
Čekada
,
P.
Panjan
,
M.
Maček
, and
P.
Šmíd
,
Surf. Coat. Technol.
151–152
,
31
(
2002
).
65.
R. G.
Coltters
and
G. R.
Belton
,
Metall. Trans. B
15
,
517
(
1984
).
66.
Y. N.
Kok
and
P. E.
Hovsepian
,
Surf. Coat. Technol.
201
,
3596
(
2006
).
67.
F.
Cheng
,
Y.
Wang
, and
T.
Yang
,
Mater. Charact.
59
,
488
(
2008
).
68.
J. Y.
Xie
,
N. X.
Chen
,
L. D.
Teng
, and
S.
Seetharaman
,
Acta Mater.
53
,
5305
(
2005
).
69.
A.
Inoue
and
T.
Masumoto
,
Scr. Metall.
13
,
711
(
1979
).
70.
B. X.
Liu
and
X. Y.
Cheng
,
J. Phys. Condens. Matter
4
,
L265
(
1992
).
71.
C.
Uebing
,
V.
Scheuch
,
M.
Kiskinova
, and
H. P.
Bonzel
,
Surf. Sci.
321
,
89
(
1994
).
72.
J.-O.
Andersson
,
Metall. Trans. A
19
,
627
(
1988
).
73.
B.
Post
,
F. W.
Glaser
, and
D.
Moskowitz
,
Acta Metall.
2
,
20
(
1954
).
74.
T.
Lundström
, in
Boron and Refractory Borides
, edited by
D. V. I.
Matkovich
(
Springer
,
Berlin
,
1977
), pp.
351
376
.
75.
G. V.
Samsonov
and
I. M.
Vinitskii
,
Handbook of Refractory Compounds
(
Springer
,
1980
), p.
182
.
76.
A.
Knappschneider
,
C.
Litterscheid
,
D.
Dzivenko
,
J. A.
Kurzman
,
R.
Seshadri
,
N.
Wagner
,
J.
Beck
,
R.
Riedel
, and
B.
Albert
,
Inorg. Chem.
52
,
540
(
2013
).
77.
R. F.
Zhang
,
X. D.
Wen
,
D.
Legut
,
Z. H.
Fu
,
S.
Veprek
,
E.
Zurek
, and
H. K.
Mao
,
Sci. Rep.
6
,
23088
(
2016
).
78.
A. R.
Oganov
and
C. W.
Glass
,
J. Chem. Phys.
124
,
244704
(
2006
).
79.
A. R.
Oganov
,
Y.
Ma
,
A. O.
Lyakhov
,
M.
Valle
, and
C.
Gatti
,
Rev. Mineral Geochem.
71
,
271
(
2010
).
80.
A. R.
Oganov
,
A. O.
Lyakhov
, and
M.
Valle
,
Acc. Chem. Res.
44
,
227
(
2011
).
81.
S.
Loubière
,
Ch.
Laurent
,
J. P.
Bonino
, and
A.
Rousset
,
Mater. Res. Bull.
30
,
1535
(
1995
).
82.
83.
R.
Kieffer
and
F.
Benesovsky
,
Hartstoffe
(
Springer-Verlag
,
Wien
,
1963
).
84.
J. J.
English
,
Binary and Ternary Phase Diagrams of Columbium, Molybdenum, Tantalum, and Tungsten
(
PN
,
1963
); includes 1961 First Report edition.
85.
L.
Kaufman
,
B.
Uhrenius
,
D.
Birnie
, and
K.
Taylor
,
Calphad
8
,
25
(
1984
).
86.
H.
Duschanek
and
P.
Rogl
,
J. Phase Equilibria
16
,
150
(
1995
).
87.
H.
Okamoto
and
T. B.
Massalski
,
J. Phase Equilibria
15
,
500
(
1994
).
88.
H. P.
Woods
,
F. E.
Wawner
, and
B. G.
Fox
,
Science
151
,
75
(
1966
).
89.
X.-Q.
Chen
,
C. L.
Fu
,
M.
Krčmar
, and
G. S.
Painter
,
Phys. Rev. Lett.
100
,
196403
(
2008
).
90.
H.-Y.
Zhang
,
F.
Xi
,
Z.-Y.
Zeng
,
X.-R.
Chen
, and
L.-C.
Cai
,
J. Phys. Chem. C
121
,
7397
(
2017
).
91.
M.
Frotscher
,
W.
Klein
,
J.
Bauer
,
C.-M.
Fang
,
J.-F.
Halet
,
A.
Senyshyn
,
C.
Baehtz
, and
B.
Albert
,
Z. Anorg. Allg. Chem.
633
,
2626
(
2007
).
92.
D. R.
Veblen
,
Am. Mineral.
76
,
801
(
1991
).
93.
M.
Kayhan
,
E.
Hildebrandt
,
M.
Frotscher
,
A.
Senyshyn
,
K.
Hofmann
,
L.
Alff
, and
B.
Albert
,
Solid State Sci.
14
,
1656
(
2012
).
94.
A.
Chretien
and
J.
Helcorsky
,
C.R. Acad. Sci.
252
,
742
(
1961
).
95.
H.
Nowotny
,
H.
Haschke
, and
F.
Benesovsky
,
Monatsh. Chem.
98
,
547
(
1967
).
96.
E.
Rudy
,
F.
Benesovsky
, and
L.
Toth
,
Z. Metallkd.
54
,
345
(
1963
).
97.
T.
Lundström
and
I.
Rosenberg
,
J. Solid State Chem.
6
,
299
(
1973
).
98.
M.
Xie
,
R.
Mohammadi
,
Z.
Mao
,
M. M.
Armentrout
,
A.
Kavner
,
R. B.
Kaner
, and
S. H.
Tolbert
,
Phys. Rev. B
85
,
064118
(
2012
).
99.
R.
Mohammadi
,
M.
Xie
,
A. T.
Lech
,
C. L.
Turner
,
A.
Kavner
,
S. H.
Tolbert
, and
R. B.
Kaner
,
J. Am. Chem. Soc.
134
,
20660
(
2012
).
100.
X.
Cheng
,
W.
Zhang
,
X.-Q.
Chen
,
H.
Niu
,
P.
Liu
,
K.
Du
,
G.
Liu
,
D.
Li
,
H.-M.
Cheng
,
H.
Ye
, and
Y.
Li
,
Appl. Phys. Lett.
103
,
171903
(
2013
).
101.
Z.
Allahyari
and
A. R.
Oganov
, e-print arXiv:1807.00854 (
2018
).
102.
Z.
Allahyari
and
A. R.
Oganov
, in
Handbook of Materials Modeling: Applications: Current and Emerging Materials
, edited by
W.
Andreoni
and
S.
Yip
(
Springer International Publishing
,
Cham
,
2018
), pp.
1
15
.
103.
V. M.
Goldschmidt
,
Trans. Faraday Soc.
25
,
253
(
1929
).
104.
A. E.
Ringwood
,
Geochim. Cosmochim. Acta
7
,
189
(
1955
).
105.
D. G.
Pettifor
,
Solid State Commun.
51
,
31
(
1984
).
106.
D. G.
Pettifor
,
J. Phys. C Solid State Phys.
19
,
285
(
1986
).
107.
S.
Wang
,
A. R.
Oganov
,
G.
Qian
,
Q.
Zhu
,
H.
Dong
,
X.
Dong
, and
M. M. D.
Esfahani
,
Phys. Chem. Chem. Phys.
18
,
1859
(
2016
).
108.
H.
Dong
,
A. R.
Oganov
,
V. V.
Brazhkin
,
Q.
Wang
,
J.
Zhang
,
M. M.
Davari Esfahani
,
X.-F.
Zhou
,
F.
Wu
, and
Q.
Zhu
,
Phys. Rev. B
98
,
174109
(
2018
).
109.
J.
Haines
,
J. M.
Léger
, and
G.
Bocquillon
,
Annu. Rev. Mater. Res.
31
,
1
(
2001
).
110.
V. V.
Brazhkin
and
A. G.
Lyapin
,
Phys.-Usp.
39
,
837
(
1996
).
111.
T.
Sasaki
,
M.
Akaishi
,
S.
Yamaoka
,
Y.
Fujiki
, and
T.
Oikawa
,
Chem. Mater.
5
,
695
(
1993
).
112.
H.
Hubert
,
L. A. J.
Garvie
,
B.
Devouard
,
P. R.
Buseck
,
W. T.
Petuskey
, and
P. F.
McMillan
,
Chem. Mater.
10
,
1530
(
1998
).
113.
A. Y.
Liu
and
M. L.
Cohen
,
Science
245
,
841
(
1989
).
114.
D. M.
Teter
and
R. J.
Hemley
,
Science
271
,
53
(
1996
).
115.
A.
Jellad
,
S.
Labdi
, and
T.
Benameur
,
J. Alloys Compd.
483
,
464
(
2009
).
116.
118.
C.-M.
Sung
and
M.-F.
Tai
,
Int. J. Refract. Metals Hard Mater.
15
,
237
(
1997
).
119.
J. M.
Léger
,
J.
Haines
, and
B.
Blanzat
,
J. Mater. Sci. Lett.
13
,
1688
(
1994
).
120.
J.
Haines
and
J. M.
Léger
,
Phys. Rev. B
48
,
13344
(
1993
).
121.
J. M.
Léger
,
P.
Djemia
,
F.
Ganot
,
J.
Haines
,
A. S.
Pereira
, and
J. A. H.
da Jornada
,
Appl. Phys. Lett.
79
,
2169
(
2001
).
122.
U.
Lundin
,
L.
Fast
,
L.
Nordström
,
B.
Johansson
,
J. M.
Wills
, and
O.
Eriksson
,
Phys. Rev. B
57
,
4979
(
1998
).
123.
J. V.
Rau
and
A.
Latini
,
Chem. Mater.
21
,
1407
(
2009
).
124.
M.
Hebbache
,
L.
Stuparević
, and
D.
Živković
,
Solid State Commun.
139
,
227
(
2006
).
125.
R. W.
Cumberland
,
M. B.
Weinberger
,
J. J.
Gilman
,
S. M.
Clark
,
S. H.
Tolbert
, and
R. B.
Kaner
,
J. Am. Chem. Soc.
127
,
7264
(
2005
).
126.
H.-Y.
Chung
,
M. B.
Weinberger
,
J.-M.
Yang
,
S. H.
Tolbert
, and
R. B.
Kaner
,
Appl. Phys. Lett.
92
,
261904
(
2008
).
127.
K.
Woo
,
K.
Lee
, and
K.
Kovnir
,
Mater. Res. Express
3
,
074003
(
2016
).
128.
Y.
Pan
,
Y. H.
Lin
,
J. M.
Guo
, and
M.
Wen
,
RSC Adv.
4
,
47377
(
2014
).
129.
L.
Wu
,
B.
Wan
,
Y.
Zhao
,
Y.
Zhang
,
H.
Liu
,
Y.
Wang
,
J.
Zhang
, and
H.
Gou
,
J. Phys. Chem. C
119
,
21649
(
2015
).
130.
P.
Wang
,
R.
Kumar
,
E. M.
Sankaran
,
X.
Qi
,
X.
Zhang
,
D.
Popov
,
A. L.
Cornelius
,
B.
Li
,
Y.
Zhao
, and
L.
Wang
,
Inorg. Chem.
57
,
1096
(
2018
).
131.
A. N.
Kolmogorov
,
S.
Shah
,
E. R.
Margine
,
A. F.
Bialon
,
T.
Hammerschmidt
, and
R.
Drautz
,
Phys. Rev. Lett.
105
,
217003
(
2010
).
132.
L.-H.
Li
,
W.-L.
Wang
,
L.
Hu
, and
B.-B.
Wei
,
Intermetallics
46
,
211
(
2014
).
133.
H.
Niu
,
X.-Q.
Chen
,
W.
Ren
,
Q.
Zhu
,
A. R.
Oganov
,
D.
Li
, and
Y.
Li
,
Phys. Chem. Chem. Phys.
16
,
15866
(
2014
).
134.
C.
Xu
,
K.
Bao
,
S.
Ma
,
Y.
Ma
,
S.
Wei
,
Z.
Shao
,
X.
Xiao
,
X.
Feng
, and
T.
Cui
,
RSC Adv.
7
,
10559
(
2017
).