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

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

Quantum playground 1 H on Si (100) B. Naydenov, M. Mantega, I. Rungger, SS and J.J. Boland, Phys. Rev. B 84 , 195321 (2011)

H on Si (100) GW Band J.E. Northrup, Phys. Rev. B 47 , 10032 (1993)

H on Si (100): single centre STM dI/dV H H 2 nm 3 nm 20 nm

H on Si (100): single centre Scattering analysis

H on Si (100): heterostructures Experiment Theory

H on Si (100): heterostructures PRB 84 , 195321 (2011) � Nano Lett. 15 , 2881 (2015)

Quantum playground 2 Topological surfaces Awadhesh Narayan, Ivan Rungger and SS, PRB 86 , 201402(R) (2012); PRB 90 , 205431 (2014)

Scattering at topological surfaces Sb (111) Nature 466 , 343 (2012) Simulated ARPES PRB 86 , 201402(R) (2012)

Scattering at topological surfaces Sb (111): scattering at step edge Transport along GM

Scattering at topological surfaces Sb (111): scattering at step edge

Scattering at topological surfaces Sb (111): scattering at step edge

Can we find new quantum playgrounds ?

The question Suppose you have a new application … . what is its ideal material(s) ? Take the example of magnetism … . Fe, Co, Ni, Nd 2 Fe 14 B, LaMnO 3 , Fe 3 O 4 … . ~2,000

Magnetism is rare The discover a new useful magnet is a rare event Fe 3 O 4

Magnetism is complicated SrCrO 3 SrMnO 3 SrFeO 3 T N =-230C T N =-10C T N =-140C SrTcO 3 SrMoO 3 SrRuO 3 T N =500C T C =-100C SrMO 3 C. Franchini, T. Archer, J. He, X.-Q. Chen, A. Filippetti and S. Sanvito, Phys. Rev. B 83 , 220402(R) (2011)

The magnetic genome project with Stefano Curtarolo, Duke

The magnetic genome project Finding descriptors Materials selection Search the database for 1) new materials, 2) physical insights Database Creation (AFLOW) Rational materials storage Creating searchable database where to store information Virtual Materials Growth 1) Simulating existing materials 2) Simulating new materials Robust electronic structure method: density functional theory (VASP)

The magnetic genome project Finding descriptors Materials selection Search the database for 1) new materials, 2) physical insights Database Creation (AFLOW) Rational materials storage Rational materials storage Creating searchable database Creating searchable database where to store information where to store information Virtual Materials Growth Virtual Materials Growth 1) Simulating existing materials 1) Simulating existing materials 2) Simulating new materials Robust electronic structure method: 2) Simulating new materials density functional theory (VASP)

The AFLOW consortium www.aflowlib.org 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)

The magnetic genome project Virtual Materials Growth (existing materials) Only ~150,000 are known to us 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

The magnetic genome project Virtual Materials Growth (existing materials) 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.) 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)

Heusler alloys ~250 known … ~1000 claimed … ~90 magnetic …

Heusler alloys ~236,000 calculated !!

The magnetic genome project Rational materials storage www.aflowlib.org 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)

… and one theory for find them all Comp. Mat. Sci. 49 , 299-312 (2010)

The magnetic genome project Finding descriptors Materials selection Materials selection Search the database for 1) new Search the database for 1) new materials, 2) physical insights materials, 2) physical insights Database Creation (AFLOW) Rational materials storage Creating searchable database where to store information Virtual Materials Growth 1) Simulating existing materials 2) Simulating new materials Robust electronic structure method: density functional theory (VASP)

A look at the full database Property : Can be made ? Descriptor 0: Enthalpy of formation Energy (Ni 2 MnAl) < Energy (2Ni + Mn +Al) Total Unique Possible Possible Magnetic 235,253 105,212 35,602 6,778

Stability analysis Descriptor 1: Enthalpy of formation Mn Ni 2 MnAl 2 Ni + Mn + Al 2 Ni + MnAl MnAl Ni 2 MnAl 1/2 (MnNi 3 + NiAl + MnAl) MnNi 3 Ni 2 MnAl Al NiAl Ni

Stability analysis (e)$ This is very much on-going Ni - Mn - Al

TM 3 Look at the transition metal intermetallics 36,540

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

Entropic temperature Descriptor 2: Entropic temperature

Entropic temperature Descriptor 2: Entropic temperature N =8776 N =248 T S T S Weibull distribution

Critical temperature magnetism Descriptor 3: Critical temperature Known Heusler ferromagnets Co 2 XY Generalized regression model based on valence, volume, spin decomposition Fe 2 Mn Y Ni 2 Mn Y Prediction of T C Mn 2 XY Material V (Å) µ Δ E (eV) T … .. T Rh 2 Mn Y Co 47.85 2.0 -0.30 3007 352 Mn 48.93 2.0 -0.32 3524 760 Cu 2 Mn Y … … … … … … Pd 2 Mn Y Mn 54.28 9.03 -0.17 1918 ? Au 2 Mn Y

Analysis Co 2 XY Mn 2 XY X 2 Mn Y

Co 2 YZ 6 Co 2 FeSi 1200 Co 2 FeSi Co 2 FeGa Co 2 YZ Co 2 AB 1 5 Co 2 FeAl 1000 Co 2 MnTi Co 2 MnTi Co 2 MnSi Co 2 MnTi Co 2 MnGe Co 2 MnAl/Co 2 MnGa 4 Co 2 MnSn 800 m ( µ B ) T C (K) Co 2 MnAl/Co 2 MnGa Co 2 CrGa Slater- 3 600 Pauling Co 2 VZn Co 2 CrGa Co 2 NbZn Co 2 VGa/Co 2 TiGe Co 2 VGa/Co 2 TiGe 2 Co 2 TaZn Co 2 TaZn Co 2 NbAl 400 Co 2 VAl Co 2 CrAl Co 2 NbAl Co 2 VZn Co 2 VAl Co 2 CrAl Co 2 NbZn Co 2 TaAl Co 2 VSn 1 200 Co 2 TiGa Co 2 AB 3 Co 2 TiGa Co 2 TiAl Co 2 NbSn Co 2 NbSn Co 2 TiAl Co 2 TaZn Co 2 VSn Co 2 AB 2 0 0 25 26 27 28 29 30 25 26 27 28 29 30 N V N V m X 2 YZ = N V -24

Co 2 YZ Slater-Pauling

X 2 Mn Z 5 600 X 2 Mn Z 4 Rh 2 MnTi Rh 2 MnZr Rh 2 MnSc Pd 2 MnCu 3 400 Pd 2 MnZn m ( µ B ) T C (K) Rh 2 MnHf Rh 2 MnZn Pt 2 MnZn N V = 27 Ru 2 MnV Pd 2 MnAu = 28 Ru 2 MnTa = 29 2 Ru 2 MnNb = 27 = 28 = 29 200 = 30 1 = 31 = 32 Castelliz- = 33 Kanomata 0 curve 4.2 4.3 4.4 4.5 4.2 4.3 4.4 4.5 d Mn-Mn (A) d Mn-Mn (A)

X 2 Mn Z X 2 Mn Z K. Shirakawa et al., J. Magn. Magn. Mater. 70 , 421 (1987)

Mn 2 YZ 200 Inverse Heusler 100 Mn 2 YZ Mn 2 PtGa (2236) Mn 2 PtIn (841) 0 Regular Heusler ∆ H (meV) Mn 2 CoCr (529) -100 Mn 2 PtV (3353) -200 Mn 2 PtPd (3218) Mn 2 PtCo (1918) -300 Mn 2 PtRh (3247) Co 2 XY -400 Mn 2 XY -500 45 50 55 60 65 3 ) V (A

OK, but does all that work?

Co 2 MnTi Co 2 MnTi T Cmeasured = 940K T Cpredicted = 938K Prepared by arc melting in an Ar atmosphere Courtesy J.M.D. Coey’s Lab (P. Tozman, M. Venkatesan)

������������������������������� �������� ��� � �� ������ Mn 2 PtPd ���� � �� ������� T N1measured = 67K �� �� � ��� ��� ��� ��� ��� ��� ��� ��������������� T N1measured = 350K Complex antiferromagnetic order Courtesy J.M.D. Coey’s Lab (P. Tozman, M. Venkatesan)

Bottom line … . Did we find one ?

Duke Team: TCD Team: Tom Archer, Anurag Tiwari, Mario Stefano Curtarolo, Junkai Xue, Kevin Rasch, Corey Oses Zic, Awadhesh Narayan, Ivan Rungger, Mauro Mantega

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