The high-throughput highway to computational materials ...
Transcript of The high-throughput highway to computational materials ...
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innovating nanoscience
The high-throughput highway to computational materials design: finding new magnets Stefano Sanvito ([email protected]) School of Physics and CRANN, Trinity College Dublin, IRELAND
UCD 2015 January 2015
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It is important to find new magnets
Novel rare-earth-free permanent magnets
US permanent magnets market ~10B$
4 Be 9.01
12Mg 24.21
2 He 4.00
10Ne 20.18
24Cr 52.00
19K 38.21
11Na 22.99
3 Li 6.94
37Rb 85.47
55Cs 132.9
38 Sr 87.62
56Ba 137.3
59Pr 140.9
1 H 1.00
5 B 10.81
9 F 19.00
17Cl 35.45
35Br 79.90
21Sc 44.96
22Ti 47.88
23V 50.94
26Fe 55.85
27Co 58.93
28Ni 58.69
29Cu 63.55
30Zn 65.39
31Ga 69.72
14Si 28.09
32Ge 72.61
33As 74.92
34Se 78.96
6 C 12.01
7 N 14.01
15P 30.97
16S 32.07
18Ar 39.95
39 Y 88.91
40 Zr 91.22
41 Nb 92.91
42 Mo 95.94
43 Tc 97.9
44 Ru 101.1
45 Rh 102.4
46 Pd 106.4
47 Ag 107.9
48 Cd 112.4
49 In 114.8
50 Sn 118.7
51 Sb 121.8
52 Te 127.6
53 I 126.9
57La 138.9
72Hf 178.5
73Ta 180.9
74W 183.8
75Re 186.2
76Os 190.2
77Ir 192.2
79Au 197.0
61Pm 145
70Yb 173.0
71Lu 175.0
90Th 232.0
91Pa 231.0
87Fr 223
88Ra 226.0
89Ac 227.0
62Sm 150.4 105
66Dy 162.5 179 85
67Ho 164.9 132 20
68Er 167.3 85 20
58Ce 140.1 13
8 O 16.00 35
65Tb 158.9 229 221
64Gd 157.3
63Eu 152.0 90
66Dy 162.5 179 85
Atomic symbol Atomic Number Atomic weight
Antiferromagnetic TN(K) Ferromagnetic TC(K)
Cost Periodic Table
80Hg 200.6
36Kr 83.80
54Xe 83.80
81Tl 204.4
82Pb 207.2
83Bi 209.0
84Po 209
85At 210
86Rn 222
Metal
Radioactive
Nonmetal
BOLD Magnetic atom
25Mn 55.85 96
20Ca 40.08
13Al 26.98
69Tm 168.9 56
312 96
36
78Pt 195.1
1043 1390 629
60Nd 144.2 19
292
< $10/kg $10 - 100/kg $100 - 1000/kg $1000 - 10000/kg >$10000/kg
92U 238.0
93Np 238.0
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It is important to find new magnets
Data storage industry has multiple requirements
❑ High TC (>600K) ❑ High spin polarization (P~100%) ❑ Low Gilbert damping ❑ Low saturation magnetization ❑ Compatible with epitaxial growth of oxides
MRAM / STT Oscillators
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Magnetism is complicated
SrMO3
SrCrO3 TN=-230C
SrMoO3
SrMnO3 TN=-10C
SrRuO3 TC=-100C
SrFeO3 TN=-140C
SrTcO3 TN=500C
C. Franchini, T. Archer, J. He, X.-Q. Chen, A. Filippetti and S. Sanvito, Phys. Rev. B 83, 220402(R) (2011)
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Magnetism is rare
The discover a new useful magnet is a rare event
SrTcO3
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Bottom line ….
We are looking for a needle in haystack
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The magnetic genome project
with Stefano Curtarolo, Duke
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The magnetic genome project
Virtual Materials Growth 1) Simulating existing materials 2) Simulating new materials
Rational materials storage Creating searchable database where to store information
Materials selection Search the database for 1) new materials, 2) physical insights
Robust electronic structure method: density functional theory (VASP)
Database Creation (AFLOW)
Finding descriptors
Virtual Materials Growth 1) Simulating existing materials 2) Simulating new materials
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The magnetic genome project
Virtual Materials Growth (existing materials)
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The magnetic genome project
ICSD: Inorganic Crystal Structure Database !• 1,616 crystal structures of the elements • 28,354 records for binary compounds • 55,436 records for ternary compounds • 54,144 records for quarternary and quintenary • About 113,000 entries (75.6%) have been assigned a structure type. • There are currently 6,336 structure prototypes. !
• Lots of redundancy/mistakes
Virtual Materials Growth (existing materials)
Only ~150,000 are known to us
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The magnetic genome project
Duke calculated single elements, binary, ternary and some quaternary (about 500,000)
Calculations:!• AFLOW manages the run (large code) • DFT done with VASP (pseudo-potential, plane-wave) • Calculations at the DFT GGA-PBE level !• Relaxation performed à new space group worked out • Basic electronic structures collected (including: spin-polarization, effective mass, magnetic moment, etc.)
Virtual Materials Growth (existing materials)
S. Curtarolo, W. Setyawan, G. L. W. Hart, M. Jahnatek, R. V. Chepulskii, R. H. Taylor, S. Wang, J. Xue, K. Yang, O. Levy, M. Mehl, H. T. Stokes, D. O. Demchenko, and D. Morgan, Comp. Mat. Sci. 58, 218 (2012)
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Heusler alloys
~250 known …
~1200 claimed …
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Heusler alloys
~236,000 calculated !!
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The magnetic genome project
Virtual Materials Growth 1) Simulating existing materials 2) Simulating new materials
Rational materials storage Creating searchable database where to store information
Materials selection Search the database for 1) new materials, 2) physical insights
Robust electronic structure method: density functional theory (VASP)
Database Creation (AFLOW)
Finding descriptors
Rational materials storage Creating searchable database where to store information
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The magnetic genome project
Rational materials storage
S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R.H. Taylor, L.J. Nelson, G.L.W. Hart, S. Sanvito, M. Buongiorno-Nardelli, N. Mingo, O. Levy, Comp. Mat. Sci. 58, 227 (2012)
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… and one theory for find them all
Comp. Mat. Sci. 49, 299-312 (2010)
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The magnetic genome project
Virtual Materials Growth 1) Simulating existing materials 2) Simulating new materials
Rational materials storage Creating searchable database where to store information
Materials selection Search the database for 1) new materials, 2) physical insights
Robust electronic structure method: density functional theory (VASP)
Database Creation (AFLOW)
Finding descriptors
Materials selection Search the database for 1) new materials, 2) physical insights
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Searching among transition metals
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A look at the full database
Descriptor 0: Enthalpy of formation
Energy (Ni2MnAl) < Energy (2Ni + Mn +Al)
Property: Can be made ?
Total
235,253
Possible
35,602
Unique
105,212 6,778
Possible Magnetic
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Stability analysis
Descriptor 1: Enthalpy of formation
Al Ni
Mn
Ni2MnAl
2 Ni + Mn + Al
Ni2MnAl
2 Ni + MnAlMnAl
MnNi3
NiAl
1/2 (MnNi3 + NiAl + MnAl)
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Stability analysis
Ni - Mn - Al
This is very much on-going
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TM3
Look at the transition metal intermetallics
36,540
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In summary …
36,540 possible à 249 stable
23 magnetic
236,000 possible à 1550 stable
138 magnetic
Extrapolating
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Entropic temperature
Descriptor 2: Entropic temperature
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Entropic temperature
Descriptor 2: Entropic temperature
N=8776 N=249
TS TS
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Critical temperature magnetism
Descriptor 3: Critical temperature
Known Heusler ferromagnets
Co2XY
Mn2XY
Ni2MnY
Rh2MnY
Cu2MnYPd2MnYAu2MnY
Fe2MnYGeneralized regression model based on valence, volume, spin decomposition
Prediction of TC
Material V (Å) µ ΔE (eV) T ….. T
Co 47.85 2.0 -0.30 3007 352
Mn 48.93 2.0 -0.32 3524 760
… … … … … …Mn 54.28 9.03 -0.17 1918 ?
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Analysis
Co2XY
Mn2XY
X2MnY
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25 26 27 28 29 30
NV
0
1
2
3
4
5
6
m (µ
B)
25 26 27 28 29 30
NV
0
200
400
600
800
1000
1200
T C (K
)
Co2MnTi
Co2FeSi
Co2AB 1
Co2CrGa
Co2MnAl/Co2MnGa
Co2NbAl
Co2VSn
Co2NbSn
Co2VZnCo2NbZnCo2TaZn
Co2VGa/Co2TiGe
Co2VAl
Co2AB 2
Co2TiGaCo2TiAl
Co2FeSi
Co2MnTi Co2MnTi
Co2FeGa
Co2FeAlCo2MnSi
Co2MnGe
Co2MnSn
Co2MnAl/Co2MnGa
Co2CrGa
Co2NbAl
Co2NbSn
Co2CrAl
Co2VSn
Co2VGa/Co2TiGe
Co2VAlCo2TaAlCo2AB 3
Co2VZnCo2NbZn
Co2TaZn
Co2TaZnCo2TiAlCo2TiGa
Co2CrAl
Co2XY
Slater-Pauling
mX2YZ=NV-24
Co2XY
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Co2XY
Slater-Pauling
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X2MnY
52 54 56 58 60 62 64 66 68
V (A3)0
100
200
300
400
500
600
700
T C(K
)
NV = 23 = 27 = 28 = 29 = 27 = 28 = 29 = 30 = 31 = 32 = 33
52 54 56 58 60 62 64 66 68
V (A3)0
1
2
3
4
5
m (µ
B)
X2MnY
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40 45 50 55 60 65 70
V (A3)
-500
-400
-300
-200
-100
0
100
200
ΔH
(meV
)
Co2XYMn2XY
Full Heusler
Inverse Heusler
Mn2YZ
Mn2YZMn2PtGa
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Mn3Ga
Mn3Ga
K. Rode et al., Phys. Rev. B 87, 184429 (2013)
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Is that all ?
?
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Tetragonal distortion
Mn3Ga Mn3Ge
Ni2MnGa Ni2MnSn Ni2MnIn
Mn2NiGa !Co2NbSn
Rh2VSn Rh2CrSn Rh2FeSn Rh2CoSn
Pd2NbSn Pd2TbSn Pd2DySn
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Tetragonal distortion
Little magnetic anisotropy in cubic symmetry. Tetragonal distortion does not much better!
T. Graf et al., in Handbook of Magnetic Materials, Elsevier (2013)
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Tetragonal distortion
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Mechanism for tetragonal distortionD
OS
Energy
EF
Non-magnetic & non-distorted
DO
S
Energy
EF
Magnetic & non-distorted
DO
S
Energy
EF
Magnetic & Distorted
Stoner Criterion vs band Jahn-Teller distortion
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Tetragonal distortion
Among our 23:
2 turn diamagnetic
Co2NbZnCo2TaZn
3 remain magnetic
X2MnY
PF~0, TC~300-400, TN~2000-5000
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OK, but does all that work?
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Co2MnTi
Courtesy J.M.D. Coey’s Lab
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Bottom line ….
Did we find one ?
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TCD Team: Duke Team:Tom Archer, Anurag Tiwari, Mario Zic
Stefano Curtarolo, Junkai Xue, Kevin Rasch