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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)

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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)
  2. Theory & Simulation of Crystals

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

  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)
  6. Webinar Outline A. Past Interacting ions and electrons B. Future

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

    Power of data and statistics
  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
  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
  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
  21. Machine Learning (ML) Chemistry https://www.coursera.org/specializations/mathematics-machine-learning

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

  23. Open Tools Example of Python tools that can benefit all

    research (whether “theory” or “experiment”) Transform Model Visualise
  24. None
  25. 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/
  26. 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
  27. Thermoelectric Devices

  28. Energy Storage Devices

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

  30. 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)
  31. 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
  32. 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!