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Materials modelling: From atoms to solar cells

Aron Walsh
February 26, 2018

Materials modelling: From atoms to solar cells

Lecture given for the Centre for Doctoral Training in New and Sustainable Photovoltaics (at the University of Bath)

Aron Walsh

February 26, 2018
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  1. CDT in New and Sustainable Photovoltaics (2018) Materials Modelling: From

    Atoms to Solar Cells Prof. Aron Walsh Department of Materials Imperial College London https://wmd-group.github.io @lonepair
  2. 2018 Module Team Aron Walsh Professor at Imperial College London

    Daniel Davies PhD Student in CDT for Sustainable Chemical Technologies at Bath
  3. Session Aim Background: Materials modelling is widely used as a

    tool for characterisation and prediction in materials science. There is an expanding literature on solar energy (e.g. active layers, interfaces, transparent conducting oxides). Aim: A basic understanding of terms and concepts, with the ability to critically assess results from research papers in your field.
  4. Modelling Solar Cells Active Layer • Electronic structure • Optical

    properties • Electron transport • Defect states Front Contact • Band offsets • Interfacial states • Interfacial dipoles • Modification layers Back Contact • Band offsets • Ion diffusion • Interfacial reactions • Modification layers Device Modelling • Carrier collection • J–V response • Efficiency losses • Layer optimisation
  5. Session Outline Materials Modelling Part 1 Fundamentals (AW) Equations, codes,

    databases Part 2 Advanced (DD) High-throughput and machine learning
  6. Lecture Outline Materials Modelling 1. Theory: What Equations to Solve

    2. Practice: Codes & Supercomputers 3. Interactive: Databases 4. Application: Halide Perovskites
  7. First-Principles Materials Modelling What? Simulate the properties of materials using

    the Schrödinger equation and chemical composition as the sole input Why? Accurate, unbiased, and predictive When? If such calculations are feasible and meaningful How? Digital computers, clever algorithms, common sense, and scientific rigor Source: http://stefano.baroni.me/presentations/
  8. First-Principles Workflow Structure Properties Input: Output: William Hamilton (Dublin, 1805)

    Hamiltonian (ions and electrons) William Bragg (Wigton, 1862) X-ray Diffraction (unit cells) Physical Chemistry (stimuli) Neville Mott (Leeds, 1905)
  9. Quantum Mechanics ˆ HΨ = EΨ Kinetic and Potential Energy

    Operators ˆ H = ˆ T + ˆ V Non Relativistic Relativistic Schrödinger (1887, Vienna) Dirac (1902, Bristol) Extra terms: scalar relativistic spin-orbit coupling
  10. Electronic Structure Techniques E[Ψ] → E[ρ] Density based quantum mechanics

    Wavefunction based quantum mechanics Methods Hartree-Fock Møller–Plesset Configuration Interaction Methods Thomas–Fermi Density Functional Dynamical Mean Field
  11. Density Functional Theory (DFT) Source: F. Bechstedt – Many-body Approach

    to Electronic Excitations (2015) Hohenberg-Kohn (1964); Kohn-Sham (1965)
  12. Kohn-Sham DFT (1965) Use one-electron Ψ i that reproduce interacting

    ρ Core Electrons all-electron pseudopotential frozen-core Hamiltonian non-relativistic scalar-relativistic spin-orbit coupling Periodicity 0D (molecules) 1D (wires) 2D (surfaces) 3D (crystals) Electron Spin restricted unrestricted non-collinear Basis Set plane waves numerical orbitals analytical functions Functional beyond…….. hybrid-GGA meta-GGA GGA LDA QMC GW RPA TD-DFT
  13. Materials Modelling with DFT Input Chemical Structure or Composition Output

    Total Energy + Electronic Structure Structure atomic forces equilibrium coordinates atomic vibrations phonons elastic constants Thermodynamics internal energy (U) enthalpy (H) free energy (G) activation energies (ΔE) Electron Energies density of states band structure effective mass tensors electron distribution magnetism Excitations transition intensities absorption spectra dielectric functions spectroscopy
  14. Bloch Waves Felix Bloch (1928) Periodic Electronic Wavefunctions Crystal wavefunction

    Periodic cell potential Plane waves 4.3. DENSITY-FUNCTIONAL THEORY: IMPLEMENTATION 55 Bloch waves describe local (intra unit cell) and long-range (inter unit cell) interactions in a crystal
  15. Bloch Waves Felix Bloch (1928) Periodic Electronic Wavefunctions Crystal wavefunction

    Periodic cell potential Plane waves 4.3. DENSITY-FUNCTIONAL THEORY: IMPLEMENTATION 55 λ=2π/k k-point Electron wavevector Crystal momentum
  16. Band Structure: GaAs Fundamentals of Semiconductors Yu and Cardona (Springer,

    1995) Koster Notation (group theory) Brillouin Zone Unit Cell
  17. Band Structure: Halide Perovskite Relativistic GW Theory Physical Review B

    89, 155204 (2014) CH3 NH3 PbI3 Conduction Band Valence Band Electronic Configuration: PbII [5d106s26p0]; I-I [5p6]
  18. Lecture Outline Materials Modelling 1. Theory: What Equations to Solve

    2. Practice: Codes & Supercomputers 3. Interactive: Databases 4. Application: Halide Perovskites
  19. Some Popular DFT Packages • CASTEP (Plane wave basis set)

    • CP2K (Mixed Gaussian/plane waves) • FHI-AIMS (Numeric orbitals) • GPAW (Numeric orbitals) • QUANTUM-ESPRESSO (Plane waves) • SIESTA (Numeric orbitals) • VASP (Plane waves) • WIEN2K (Augmented plane waves) [Open Source] [Open Source] [Open Source] [Open Source]
  20. GPAW: Open Source and Python https://wiki.fysik.dtu.dk/gpaw/ Large community of researchers.

    Free and open source! • Links to Atomistic Simulation Environment • Written in C and Python • Easy to use • pip install gpaw
  21. Vienna Ab Initio Simulation Package Widely used FORTRAN code from

    Austria (Prof. Georg Kresse) • License fee ~€5000 (small academic group) • Site: http://www.vasp.at • Forum: http://cms.mpi.univie.ac.at/vasp-forum • Wiki: http://cms.mpi.univie.ac.at/wiki • Many pre- and post-processing tools • Visualisation: http://jp-minerals.org/vesta A popular package because of reliable pseudopotentials for periodic table (benchmarked against all-electron methods)
  22. Compiling Scientific Codes General Requirements: Program source code (e.g. x.f,

    x.f90, x.c); Makefile or configure script; Math libraries; Fortran or C compiler Common Compilers: Intel Fortran (ifort); Portland Group (pgf90); Gnu-Fortran (gfortran); Pathscale (pathf90); Generic links (f77 or f90) Common Libraries: LAPACK (Linear algebra - diagonalisation) - ScaLAPACK (Distributed memory version) BLAS (Linear algebra – vector / matrix multiplication) BLACS (Linear algebra communication subprograms) Examples: MKL (Intel); ACML (AMD); GotoBLAS
  23. VASP Input Files • POSCAR (“Position Card”) • POTCAR (“Potential

    Card”) • INCAR (“Input Card”) • KPOINTS (k-point Sampling) All four files should be in the same directory for VASP to run successfully Caution: The order of the elements in POTCAR must be the same as POSCAR
  24. VASP Output Files • OUTCAR (“Output Card”) • CONTCAR (“Continue

    [Positions] Card”) • CHGCAR (“Charge Density Card”) • vasprun.xml (Auxiliary output as xml) A number of additional files that are generated depending on flags set in INCAR Caution: If NSW > 0, a number of the properties are averaged over past structures (rerun with NSW=0 at end)
  25. Choice of Exc Takes Experience INCAR (Partial) KPOINTS Recommended: PBEsol

    (GGA for solids) & HSE06 (screened hybrid GGA) Journal of Chemical Physics 123, 174101 (2005) Often a (computational) cost vs accuracy tradeoff
  26. Electronic Spectroscopy INCAR (Partial) KPOINTS Source: Patrick Rinks (FHI-AIMS Workshop

    2011) Electronic band gap ≠ Optical band gap N-1 quasi-particle N+1 quasi-particle (electron + interaction with environment) N excitation (e-h interaction)
  27. Electronic Spectroscopy: HgO INCAR (Partial) KPOINTS Chemical Physics Letters 399,

    98 (2004) [1st Publication!] XPS (weighted DOS) O K XES (O 2p DOS)
  28. Electronic vs Optical: In2 O3 INCAR (Partial) KPOINTS Physical Review

    Letters 100, 167402 (2008) Large difference in optical and electronic band gap is one reason why it is a high performance TCO
  29. Lecture Outline Materials Modelling 1. Theory: What Equations to Solve

    2. Practice: Codes & Supercomputers 3. Interactive: Databases 4. Application: Halide Perovskites
  30. New Paradigm in Science Global Movement Associated with Databases, #OpenData

    and #OpenScience Agrawal and Choudhary, APL Materials 4, 053208 (2016)
  31. Computational Property Databases • http://aflowlib.org • https://materialsproject.org • http://repository.nomad-coe.eu •

    http://materials.nrel.gov • http://oqmd.org • http://phonondb.mtl.kyoto-u.ac.jp • http://www.tedesignlab.org Popular databases include:
  32. Crystal Structure Task What is the shortest Cu–S bond length

    in CuGaS2 ? 1. Log on to: https://materialsproject.org 2. Download the most stable crystal structure of CuGaS2 3. Open in VESTA (http://jp-minerals.org/vesta/en/) and draw bonds [EDIT: BONDS]
  33. Lecture Outline Materials Modelling 1. Theory: What Equations to Solve

    2. Practice: Codes & Supercomputers 3. Interactive: Databases 4. Application: Halide Perovskites
  34. Hybrid Organic–Inorganic Perovskites Brief History (1958) – Photoconductivity in CsPbI3

    (Møller) (1978) – Synthesis of CH3 NH3 PbI3 (Weber) (1994) – Metallic transition in CH3 NH3 SnI3 (Mitzi) (2009) – Perovskite dye cell (Miyasaka) (2012) – Planar thin-film solar cell (Snaith) Inorganic CsPbI3 Hybrid CH3 NH3 PbI3 or MAPI
  35. Why Halide Perovskites? Essentials for Solar Cells • Strong optical

    absorption (Eg ~ 1.6 eV) • Light electron and hole masses (conductive) • Easy to synthesise (cheap and scalable) Advanced Features • Dielectric screening: carrier separation (weak excitons) and transport (low scattering rates) • Slow e-h recombination: low losses, large VOC o Relativistic effects – spin-orbit coupling o Polar domains – dynamic fluctuations o Phonon scattering – non-radiative limits
  36. Perovskites: Model vs Reality Plastic crystal behaviour probed by Quasi-Elastic

    Neutron Scattering (P. Barnes, DOI: 10.1038/ncomms8124); 2D IR Spectroscopy (A. Bakulin, DOI: 10.1021/acs.jpclett.5b01555); Inelastic X-ray Scattering (S. Billinge, DOI: 10.1021/acsenergylett.6b00381) with simulations
  37. Dynamic Processes in Perovskites Faster (fs) Slower (s) Electrons and

    Holes Effective semiconductors Lattice Vibrations Symmetry breaking and carrier separation Molecular Rotations Large static dielectric constant Ions and Charged Defects “Self healing” and hysteresis J. M. Frost and A. Walsh, Acc. Chem. Res. 49, 528 (2016)
  38. “Giant Dielectric Constant” JPCM 20, 191001 (2008) JPCL 5, 2390

    (2014) J. M. Frost and A. Walsh, Acc. Chem. Res. 49, 528 (2016)
  39. “Giant Dielectric Constant” JPCM 20, 191001 (2008) JPCL 5, 2390

    (2014) J. M. Frost and A. Walsh, Acc. Chem. Res. 49, 528 (2016)
  40. Mixed Ion–Electron Conductors Evidence and Consequences • Current-voltage hysteresis [Snaith

    et al, JPCL (2014); Unger et al, EES (2014)] • Rapid chemical conversion between halides [Pellet et al, CM (2015); Eperon et al, MH (2015)] • Photoinduced phase separation [Hoke et al, CS (2015); Yoon et al, ACS-EL (2016)] • Electric field induced phase separation [Xiao et al, NatM (2015); Yuan et al, AEM (2016)]
  41. Hot Polaron Cooling Long-lived hot carriers upon photoexcitation [Phonon Bottleneck]

    Science 356, 59 (2017); Science 353, 1309 (2016); Nat. Photonics 10, 53 (2016); Nat. Commun. 6, 8420 (2015)
  42. Key Factor: Thermal Conductivity Whalley, Skelton, Frost, Walsh, Physical Review

    B 94, 220301(R) (2016) T = 300K GaAs 38 (calculated) 45 (measured) CdTe 9 (calculated) 7 (measured) MAPI 0.05 (calculated) ~0.5 (measured) Calculated lattice thermal conductivity (3-phonon scattering) 46,800 DFT calculations!
  43. Hot Polaron Cooling Rate Excess energy contained in polaron, with

    slow exchange to the bulk crystal Frost, Whalley, Walsh, ACS Energy Letters 2, 2647 (2017) Low Density n < 1018 cm-3 High Density n > 1018 cm-3 (Laser source) Notebooks: https://github.com/WMD- group/hot-carrier-cooling
  44. Conclusions • Many materials modeling approaches for different length and

    time scales • First-principles techniques can accurately predict structure and properties • Materials data and reproducibility is becoming increasingly important Slides: https://speakerdeck.com/aronwalsh news & views e spoiled for urally occurring iodic table give y compounds ry compounds ompounds, each element inations exceed nent system. the number of ALS uest for new functionality tanding of the chemical bond, advances in synthetic chemistry, and large-scale computation, ow become a reality. From a pool of 400 unknown compositions, 15 new compounds have pt the expected structures and properties. Structural prediction Property simulation Targeted synthesis Chemical input Figure 1 | A modular materials design procedure, where an initial selection of chemical elements is subject to a series of optimization and screening steps. Each step may involve prediction of the crystal Future: