1. Understand and predict materials with atomic accuracy Giovanni Cantele Coherentia CNR-INFM and Università di Napoli “Federico II

2. Outline Our group Material science ◦computational issues ◦understand… ◦predict… Conclusions

3. Our group Permanent staff G. Iadonisi D. Ninno G. Cantele Post-doc F. Trani (now at Univ. Of Lion) I. Borriello Phd students A. Iacomino Post-degree R. D’Amico Advanced (nano)materials for electronics and sensing applications o Graphene o Hybrid organic-inorganic perovskites o Oxides surfaces and interfaces (TiO2, SnO2, SrTiO3,...) Optical and electronic properties of semiconductor nanocrystals http://www.nanomat.unina.it

4. Methods and codes Density functional theory ◦Massively parallel computing ◦Atomistic accuracy, correct chemical description of defects, surface-adsorbate interactions, functionalization,... ◦Plane-waves or localized function basis sets Home-made innovative codes: tight binding ◦Semiconductor nanocrystals composed by a huge number of atoms ◦Graphical interface, work in progress for allowing the study of more complex semiconductors (e.g. CdSe) ◦ http://www.nanomat.unina.it/index.php?n=Res.SW http://www.quantum-espresso.org

5. Computing facilities Nanomat cluster Nanomat cluster ◦31 nodes ◦Each node: Two dual-core INTEL Xeon CPU 5160 @ 3.00GHz ◦8 to 12 Gb RAM ◦Infiniband fast interconnect ◦Investment: 190 K€ CINECA

6. Atomic cores vs CPU cores! 3.9 Å14.8 Å 7.7 Å ×2 (both x and y) ×2 (both x and y)  4700 s100 s

7. Methods and codes Plane-wave codes & parallelism ◦wave functions are represented on a plane wave basis set ◦Sum over k points  divide CPUs into N p pools i1i1 i2i2 i3i3 CPU 1 CPU N CPUCPU CPUCPU CPUCPU CPUCPU CPUCPU CPUCPU Pool 1Pool N p H k1 H k2 H kM 00 0 0 0 0 0 0 0 0 0 000 0 00 0 0 0

8. Understand… Optical emission from defected oxide surfaces and hybrid perovskites

9. Nature of visible light emission in SnO 2 nanowires SnO 2 exhibit a strong visible photoluminescence, not observed in the bulk material O vacancies do play a role: direct origin of PL or trigger of other radiative processes? radiative recombination centers (“emitting centers”) “switched off” by the presence of adsorbed NO 2 molecules and removed by annealing in oxygen S. Lettieri et al, JCP (2008) Mode locked Nd:YAG C1C1 C2C2 PDPD L1L1 L2L2 L3L3 Delay generator Sync out 3w3w 2w2w Trig in Sampl e M1M1 M2M2 M3M3 M4M4 M5M5 F2F2 F1F1 PC Streak Camera

10. Nature of visible light emission in SnO 2 nanowires surface oxygen vacancies as direct origin of visible PL surface bridging oxygen vacancies in SnO 2 lead to formation of occupied and empty surface bands whose transition energies are in strong agreement with luminescence features and whose luminescence activity can be switched off by surface adsorption of oxidizing molecules stoichiometric defected

11. Organic-inorganic hybrids and interfaces: CH 3 NH 3 SnX 3 perovskites perovskite structure, corner-sharing octahedra of X anions (X=Cl, Br, I) Structural phase transitions, e.g. X=Cl cubic  rhombohedral  monoclinic  triclininc Octahedra network vs embedded cation (Cs +, CH 3 NH 3 +,...) T = 463 K T = 331 K T = 308 K

12. Organic-inorganic hybrids and interfaces: CH 3 NH 3 SnX 3 perovskites X  (M cm) measured E g (eV)calculated E g (eV) DFT-GGA + GW Br0.5 ± 0.12.15 ± 0.011.90 Cl1.4 ± 0.13.69 ± 0.053.44 CH 3 NH 3 SnX 3 The adsorption edge rapidly raises to values typical for direct band- gap crystals X = Br X = Cl F. Chiarella, PRB (2007) I. Borriello, PRB (2008)

13. Predict… Work function engineering via smart adsorbates

14.  = 0.27 D = 1.37 D  = 1.24 D  = 3.32 D = 4.25 D  = 0.0 D Electron affinity/work function variations induced by adsorbates * –  = -e n dip P/ 0 Organic-inorganic hybrids and interfaces: adsorbates on Si(001)

15. The variation in the work function is induced by the dipole layer associated with the molecular layer on the surface the dipole contribution to the work function variations are not always linearly correlated with the isolated molecule dipole G. Cantele et al, J. Phys.: Cond. Matter (2006) I. Borriello et al, PRB (2007)

16. Conclusions Material science and engineering require massive computing facilities This Open Day perspectives ◦Sharing ideas ◦Sharing CPU time…and save money while getting new one! ◦Sharing skills