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The long way to the discovery of new materials made it short - - PowerPoint PPT Presentation

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AICQT, Maynooth June 2016 innovating nanoscience The long way to the discovery of new materials made it short Stefano Sanvito (sanvitos@tcd.ie) School of Physics and CRANN, Trinity College Dublin, IRELAND Theory activity Theory activity

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innovating nanoscience

The long way to the discovery of new materials made it short

Stefano Sanvito (sanvitos@tcd.ie)

School of Physics and CRANN, Trinity College Dublin, IRELAND AICQT, Maynooth June 2016

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Theory activity

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Theory activity

Spin electronics Materials Organics

Spin-dynamics Spin-transport Transport and STM Diffusive Transport DNA sequencing 2D/topological Organic spintronics Magnetic Genoma Spin excitation/torque

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Quantum playground 1 H on Si (100)

  • B. Naydenov, M. Mantega, I. Rungger, SS and J.J. Boland, Phys. Rev. B 84, 195321 (2011)
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H on Si (100)

J.E. Northrup, Phys. Rev. B 47, 10032 (1993)

GW Band

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H on Si (100): single centre

H H

20 nm 3 nm 2 nm

STM dI/dV

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H on Si (100): single centre Scattering analysis

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H on Si (100): heterostructures Theory Experiment

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PRB 84, 195321 (2011)

  • Nano Lett. 15, 2881

(2015)

H on Si (100): heterostructures

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Quantum playground 2 Topological surfaces

Awadhesh Narayan, Ivan Rungger and SS, PRB 86, 201402(R) (2012); PRB 90, 205431 (2014)

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PRB 86, 201402(R) (2012)

Scattering at topological surfaces

Sb (111)

Simulated ARPES

Nature 466, 343 (2012)

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Scattering at topological surfaces

Sb (111): scattering at step edge

Transport along GM

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Scattering at topological surfaces

Sb (111): scattering at step edge

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Scattering at topological surfaces

Sb (111): scattering at step edge

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Can we find new quantum playgrounds ?

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The question

Suppose you have a new application …. what is its ideal material(s) ?

Fe, Co, Ni, Nd2Fe14B, LaMnO3, Fe3O4 ….

~2,000

Take the example of magnetism ….

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Magnetism is rare

The discover a new useful magnet is a rare event

Fe3O4

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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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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

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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 Rational materials storage Creating searchable database where to store information

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The AFLOW consortium

  • 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)

www.aflowlib.org

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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

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 50,000) 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 … ~1000 claimed … ~90 magnetic …

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Heusler alloys

~236,000 calculated !!

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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)

www.aflowlib.org

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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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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

2 Ni + Mn + Al Ni2MnAl

Ni2MnAl

2 Ni + MnAl

MnAl MnNi3 NiAl

1/2 (MnNi3 + NiAl + MnAl)

Ni2MnAl

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Stability analysis This is very much on-going

(e)$

Ni - Mn - Al

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TM3 Look at the transition metal intermetallics

36,540

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In summary …

36,540 possible à 248 stable 22 magnetic à 8 Robust 236,000 possible à 1550 stable 138 magnetic à 50 Robust

Extrapolating

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Entropic temperature Descriptor 2: Entropic temperature

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Entropic temperature Descriptor 2: Entropic temperature N=8776 N=248

TS TS

Weibull distribution

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Critical temperature magnetism Descriptor 3: Critical temperature Known Heusler ferromagnets Co2XY Mn2XY Ni2MnY Rh2MnY Cu2MnY Pd2MnY Au2MnY Fe2MnY Generalized 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

1 2 3 4 5 6

m (µB)

25 26 27 28 29 30

NV

200 400 600 800 1000 1200

TC (K)

Co2MnTi Co2FeSi Co2AB 1 Co2CrGa Co2MnAl/Co2MnGa Co2NbAl Co2VSn Co2NbSn Co2VZn Co2NbZn Co2TaZn Co2VGa/Co2TiGe Co2VAl Co2AB 2 Co2TiGa Co2TiAl Co2FeSi Co2MnTi Co2MnTi Co2FeGa Co2FeAl Co2MnSi Co2MnGe Co2MnSn Co2MnAl/Co2MnGa Co2CrGa Co2NbAl Co2NbSn Co2CrAl Co2VSn Co2VGa/Co2TiGe Co2VAl Co2TaAl Co2AB 3 Co2VZn Co2NbZn Co2TaZn Co2TaZn Co2TiAl Co2TiGa Co2CrAl

Co2YZ Slater- Pauling

mX2YZ=NV-24 Co2YZ

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Co2YZ Slater-Pauling

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X2MnZ

X2MnZ

4.2 4.3 4.4 4.5

dMn-Mn (A)

200 400 600

TC(K)

NV = 27 = 28 = 29 = 27 = 28 = 29 = 30 = 31 = 32 = 33

4.2 4.3 4.4 4.5

dMn-Mn (A)

1 2 3 4 5

m (µB)

Ru2MnV Pd2MnCu Rh2MnTi Pd2MnZn Pt2MnZn Ru2MnNb Ru2MnTa Rh2MnSc Pd2MnAu Rh2MnHf Rh2MnZr Rh2MnZn

Castelliz- Kanomata curve

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X2MnZ

X2MnZ

  • K. Shirakawa et al., J. Magn. Magn. Mater. 70, 421 (1987)
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Mn2YZ

Mn2YZ

45 50 55 60 65

V (A

3)

  • 500
  • 400
  • 300
  • 200
  • 100

100 200

∆H (meV)

Co2XY Mn2XY

Regular Heusler Inverse Heusler

Mn2CoCr (529) Mn2PtCo (1918) Mn2PtV (3353) Mn2PtPd (3218) Mn2PtRh (3247) Mn2PtGa (2236) Mn2PtIn (841)

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OK, but does all that work?

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Co2MnTi Courtesy J.M.D. Coey’s Lab (P. Tozman, M. Venkatesan) Prepared by arc melting in an Ar atmosphere

Co2MnTi

TCmeasured = 940K TCpredicted = 938K

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Mn2PtPd

TN1measured = 67K TN1measured = 350K

Courtesy J.M.D. Coey’s Lab (P. Tozman, M. Venkatesan) Complex antiferromagnetic

  • rder
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Bottom line …. Did we find one ?

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TCD Team: Duke Team:

Tom Archer, Anurag Tiwari, Mario Zic, Awadhesh Narayan, Ivan Rungger, Mauro Mantega

Stefano Curtarolo, Junkai Xue, Kevin Rasch, Corey Oses

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innovating nanoscience

The long way to the discovery of new materials made it short

Stefano Sanvito (sanvitos@tcd.ie)

School of Physics and CRANN, Trinity College Dublin, IRELAND AICQT, Maynooth June 2016