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Computer-Accelerated Materials Design

Computer-Accelerated Materials Design

A lunchtime webinar delivered for the Energy Futures Lab (https://www.imperial.ac.uk/energy-futures-lab/) and available on Youtube (https://youtu.be/vnSRue_Yd1I)

Aron Walsh

May 28, 2020
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  1. Computer–Accelerated
    Materials Design
    Prof. Aron Walsh
    Thomas Young Centre
    Department of Materials
    EFL Lunchtime Webinar (May 2020)

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  2. Theory & Simulation of Crystals

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  3. Decrease Technology Timeline
    J.P. Correa-Baena et al., Joule 2, 1410 (2018)
    Use simulations & statistics to reduce barriers for
    synthesis, device assembly, and diagnostics

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  4. Materials Design
    INPUT OUTPUT
    Property
    or
    Metric
    Composition
    and
    Structure

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  5. What is Computational Chemistry?
    “Using mathematical methods for the calculation
    of chemical properties or for the simulation of
    molecular/materials behaviour. It also
    includes, e.g. synthesis planning, database
    searching, combinatorial library manipulation”
    IUPAC (2014)
    IBM 5MB hard drive (1956) IBM quantum computer (2020)

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  6. Webinar Outline
    A. Past
    Interacting ions and electrons
    B. Future
    Power of data and statistics

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  7. Models of Interacting Elements
    erg was a popular
    unit of energy (10-7 J)
    J. E. Jones, Proc. Roy. Soc. A 106, 463 (1923)

    =

    !"


    #
    Repulsive Attractive
    Molecular mechanics (MM): application of classical
    mechanics to model chemical systems. Often
    analytic functions (bond stretching, bending, etc.)
    John Lennard-Jones
    (1894–1954)

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  8. Models of Interacting Elements
    Some models implemented in GULP
    https://gulp.curtin.edu.au
    Molecular mechanics (MM): application of classical
    mechanics to model chemical systems. Often
    analytic functions (bond stretching, bending, etc.)

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  9. Models of Interacting Elements
    Molecular mechanics: simulations of >1 billion
    atoms now possible (e.g. protein folding, radiation
    damage, crystallisation, correlated diffusion)
    Y. Shibuta et al, Nat. Comm. 8, 10 (2017)
    Solidification of
    Fe with Finnis -
    Sinclair potential
    using molecular
    dynamics

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  10. Models of Interacting Electrons
    Quantum mechanics (QM): numerical solutions of
    the Schrödinger, or relativistic Dirac, equation to
    describe electron distributions (chemical bonds)
    The master equations appear simple, but for >1 electron these partial
    differential equations cannot be solved exactly and require approximations
    Many-body
    wavefunction

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  11. Models of Interacting Electrons
    Density functional theory (DFT) replaces the many-body N-dimensional
    electronic wavefunction by the 3-dimensional electron density
    Quantum mechanics (QM): numerical solutions of
    the Schrödinger, or relativistic Dirac, equation to
    describe electron distributions (chemical bonds)
    Walter Kohn
    (1923–2016)

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  12. Computational Bottleneck of DFT
    O(N3) scaling due to matrix diagonalisation,
    e.g. 10⨉ atoms cost 1000⨉ more processing time
    Density functional theory
    scaling data in FHI-AIMS
    courtesy of Volker Blum
    https://aimsclub.fhi-berlin.mpg.de
    Using ELPA massively
    parallel eigensolvers
    https://gitlab.mpcdf.mpg.de/elpa

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  13. Recovering Chemical Concepts
    Covalent bond, ionic bond, oxidation state…
    Simple concepts can be difficult to extract from QM
    Five (of the many)
    ways to assign
    electron density to
    atomic centres in
    crystals
    “Oxidation states and ionicity” A. Walsh et al, Nature Mater. 17, 958 (2018)

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  14. Open Source Materials Modelling
    Selection of DFT packages for calculating
    electronic structure and properties of crystals
    Code Features Link
    ABINIT Plane wave basis set – many
    property calculators
    https://www.abinit.org
    CP2K Mixed basis set – fast for
    large and complex systems
    https://www.cp2k.org
    GPAW Flexible basis set – modern
    python interface
    https://wiki.fysik.dtu.dk/gpaw
    Quantum
    Espresso
    Plane wave basis set – many
    property calculators
    http://www.quantum-
    espresso.org
    Starting point: Atomic Simulation Environment https://wiki.fysik.dtu.dk/ase/

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  15. Open Source Materials Modelling
    Selection of first-principles properties
    that are used in materials science
    Class Property Measurement
    Structural Ground-state
    crystal structure
    X-ray/neutron/electron
    diffraction
    Vibrational Phonon frequencies
    and lifetimes
    X-ray/neutron scattering
    IR/Raman spectroscopy
    Thermodynamic Internal and free
    energy changes
    Stability and chemical
    reactivity
    Point
    Defects
    Concentrations,
    barriers, diffusion rates
    Impedance spectroscopy;
    isotope tracers, etc.
    Extended
    Defects
    Changes at surfaces
    and grain boundaries
    Tomography; microscopy;
    spatially resolved probes

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  16. Thermal Transport in NMC Batteries
    Mixtures of LiNiO2
    , LiMnO2
    , and LiCoO2
    (NMC) are
    used for high-performance Li-battery cathodes
    Hui Yang et al, ChemRxiv Preprint Server (2020)

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  17. Webinar Outline
    A. Past
    Interacting ions and electrons
    B. Future
    Power of data and statistics

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  18. Data-Driven Science
    Agrawal and Choudhary, APL Materials 4, 053208 (2016)
    Entering a new phase of scientific discovery,
    harnessing advances and tools from data science

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  19. Machine Learning (ML) Chemistry
    Statistical algorithms that learn from training data
    and build a model to make predictions
    Quick-start machine learning guide: K. T. Butler et al, Nature 559, 547 (2018)
    Applications include:
    • Faster (or better)
    computational tools
    • New knowledge from
    existing data
    • “Self-driving” research
    workflows

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  20. Machine Learning (ML) Chemistry
    Images from: https://vas3k.ru/blog/machine_learning/
    Predict a category, e.g.
    decision trees to predict
    reaction outcome
    Predict a value, e.g.
    regression to extract a
    reaction rate
    Group by similarity, e.g.
    high-throughput
    crystallography
    Maximize reward, e.g.
    reaction conditions to
    optimize yield

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  21. Machine Learning (ML) Chemistry
    https://www.coursera.org/specializations/mathematics-machine-learning

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  22. Machine Learning (ML) Chemistry
    https://xkcd.com/1838/

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  23. Open Tools
    Example of Python tools that can benefit all
    research (whether “theory” or “experiment”)
    Transform
    Model
    Visualise

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  24. Open Data
    • Reproducibility – direct comparison with
    published literature beyond static tables and
    figures, e.g. raw spectra
    • Reuse – facilitate meta-studies comparing
    results from multiple samples, e.g. variation in
    UV-vis features / band gap assignments
    • Statistical Models – power of machine learning
    depends on the quantity, quality, and diversity
    of training data
    For data-management guidance: https://www.go-fair.org/fair-principles/

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  25. Crystallography in the Lead
    https://www.ccdc.cam.ac.uk / https://checkcif.iucr.org
    Cambridge
    Structural Database
    (from 1960)
    ….
    1 million
    2019
    Human and Machine Readable
    Community Databases
    of Known Materials
    Standard Format

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  26. Thermoelectric Devices

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  27. Energy Storage Devices

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  28. Solar Cells
    https://doi.org/10.1088/2515-7655/ab2338

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  29. Unexplored Crystal Space
    D. W. Davies et al, Screening all inorganic materials, Chem 1, 617 (2016)
    Large number of candidate systems
    • Composition (AB, A2
    B3
    …)
    • Structure (polymorphs)
    • Stoichiometry (defects)
    • Morphology (facets)

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  30. Time to Curate your Data
    https://cen.acs.org/content/cen/articles/98/i20/time-
    curate-data-Use-shutdown.html
    Use lab downtime to launch a new data regime

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  31. Conclusions
    Materials Design Roadmap
    https://doi.org/10.1088/1361-6463/aad926
    Slides: https://speakerdeck.com/aronwalsh @lonepair
    There are many exciting
    developments in materials
    modelling and design. The
    transition from analytical
    to numerical methods is
    now being augmented by
    statistical approaches –
    limited by available data!
    Thanks to research group, collaboration
    network, and the EFL team!

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