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Computational materials science: From needle crystals to complex - - PowerPoint PPT Presentation

Nov 10, 2022 20 likes •194 views

Computational materials science: From needle crystals to complex polycrystalline forms L. Grnsy a,b a Wigner Research Centre for Physics, H-1525 Budapest, P. O. Box 49, Hungary b BCAST, Brunel University, Uxbridge, Middlesex UB8 3PH, U.K.

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Computational materials science: From needle crystals to complex polycrystalline forms

aWigner Research Centre for Physics, H-1525 Budapest, P. O. Box 49, Hungary bBCAST, Brunel University, Uxbridge, Middlesex UB8 3PH, U.K.

  • L. Gránásya,b

Inaugural presentation as elected member of Academia Europaea (Academy of Europe, London), Hungarian Academy of Sciences, 3 September 2017, Budapest, Hungary 1

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SLIDE 2

Complex patterns evolve due to the interplay of nucleation and growth.

  • I. Introduction: Complex polycrystalline structures

2

American Pale Ale Dirty Martini Vodka Tonic Gin Water 0p

Polycystalline matter:

  • technical alloys
  • ceramics
  • polymers
  • minerals
  • food products, etc.

In biology:

  • bones, teeth
  • kidney stone
  • cholesterol in arteries
  • amyloid plaques in Alzheimer’s disease

Also frozen drinks:

Aim of Computational Materials Physics:

To understand and predict the behavior of materials Tools: micro-, mezo- and macroscale models: ab initio, DFT, MD, PFC, PFT, CFD, etc.)

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Microstructure – Input data: free energies, diffusion coefficients,

interfacial free energies, anisotropies (  micr. models, data bases)

– Numerical solution (finite diff., spectral, …) – Mathematical model  PF theory: EOMs are

coupled nonlinear stochastic PDEs

Model Numerical solver

Structural order parameter [phase field: (r, t)]

– Computation facilities: CPU and GPU clusters 3

1p

  • II. Modeling of crystalline microstructure (m, s  cm, min)

In a few cases (metal alloys): Knowledge-based Materials Design

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SLIDE 4
  • 1. Impinging single crystals:
  • 2. Polycrystalline

growth forms:

(Growth Front Nucleation = GFN)

  • 3. Impinging polycrystalline particles:

4

3p

Classification of polycrystalline microstructures

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SLIDE 5
  • 1. Diffusional instabilities:
  • 2. Nucleation
  • of growth centers
  • homogeneous
  • heterogeneous (on particles or walls)
  • of new grains at the growth front (Growth Front Nucleation = GFN)
  • heterogeneous (particle-induced)
  • homogeneous (???)

with specific misorientation (fixed branching angle)

Contributing phenomena?

Crystal Liquid

Mullins-Sekerka instability isotropic anisotropic

5

4p

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SLIDE 6
  • 1. Diffusional instabilities:
  • 2. Nucleation of growth centers
  • homogeneous

adding noise to EOM (Phys. Rev. Lett. 2002)

  • heterogeneous

noise + appropriate BC (Phys. Rev. Lett. 2007)

  • 3. Nucleation of new grains at the growth front
  • heterogeneous

particle-induced tip-deflection (2D: Nature Mater. 2003, 3D: Europhys. Lett. 2005)

  • homogeneous I.

reduced M (2D: Nature Mater. 2004, 3D: Europhys. Lett. 2005)

  • homogeneous II.

MS minimum in fori (Phys. Rev. E 2005)

Summary: Phenomena incorporated in 2D & 3D:

isotropic anisotropic composition phase field

  • rientation

6

5p

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SLIDE 7

7

7p

  • A. Needle crystals in 2D: (kinetic & interface free energy anisotropy)
  • III. Applications

4000  4000 grid

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SLIDE 8
  • B. From needle crystal to polycrystalline spherulite:

S = 1.5 1.8 1.9 1.95 2.0 2.1 2.2

200200400 grid Triclinic crystal symmetry Ellipsoidal symmetry of kinetic anisotropy Coloring: Inclination relative to nucleated direction in deg.

S = 0.75 0.85 0.90 0.95 1.00 1.10

2D

8

8p

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  • C. Morphological

variability Experiment Simulation Experiment Simulation Description with only a few model parameters

(anisotropies, branching angle, MS well depth, …)

9

9p

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SLIDE 10

Phase-Field simulation Polarized transmission optical microscope (iPP)

Gatos et al. Macromol. (2007)

  • D. Comparison with experiment on orientation

10

9.5p

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Gradual transition from single crystal nucleus to Category 1 spherulite:

Interface breakdown Polycrystalline nucleus Experiment

Atomistic view for GFN?

10p

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  • D. Formation of spherulite by GFN

4000  4000 grid

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  • A. Nucleation ahead of growth front
  • E. Two modes of GFN in hydrodynamic Phase-Field Crystal simulations:

|g6| Orientation map Voronoi map

  • B. Formation of dislocations in cusps

HPFC

12

10.5p

Structural analysis (complex bond oder parameter):

  • j :

angle towards j-th neighbor in lab. frame

  • |g6| :

 degree of order

  • phase:

 local crystallographic orientation Voronoi analysis: 4 - grey; 5 - blue; 6 - yellow; 7 - red

2048  2048 grid 600  600 sect.

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SLIDE 13

13

  • F. GFN by interference of density waves at front in the HPFC simulation:

13p Orientation map Voronoi map

MD for 1 billion Fe atoms: Shibuta et al. Nature Comm. (2017)

Satellite grains: (red arrows) Density map 1024  1024 sect. of 2048  2048 grid Multi-orientation crystallites:

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SLIDE 14
  • I. Floating dendrites (L. Rátkai et al.)
  • II. Grain boundary dynamics (B. Korbuly et al. PRE 2017)
  • III. Anisotropic eutectics (L. Rátkai et al. JMS 2017)

14

14p

  • G. Other recent works:
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SLIDE 15

ESA website: “Space in videos”

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  • 1. Modeling of exotic microstructures:
  • Phys. Rev. Lett. 2002; Nat. Mater, 2003, 2004 ( IF = 10,8; 13,5 );
  • Mater. Sci. Eng. Rep. 2004 ( IF = 14,2 ); Europhys. Lett. 2005;
  • Phys. Rev. E 2013; Metall. Mater. Trans. A 2014;
  • J. Chem. Phys. 2015
  • 2. Application of the Phase-Field (PF) model to materials of industrial interest:
  • optimization of soft magnetic alloys via phase selection (ESA Prodex/PECS)
  • lead-free self lubricating bearing materials

(ESA Prodex)

  • high melting point alloys for gas turbine blades

(EU FP 6)

  • in-situ composites, particle-front interaction

(ESA Prodex/PECS)

  • production of metamaterials via eutectic solidification

(EU FP7)

  • 3. Molecular scale simulation of crystal nucleation (CDFT):

PRL 2011, 2012

  • Adv. Phys. 2012

( IF = 34,3 )

  • Chem. Soc. Rev. 2014

( IF = 33,4 )

  • Nat. Phys. 2014

( IF = 20,6 )

  • 4. Modeling of multi-phase flow (PF + NS, PF + LB, HPFC):

MSEA 2005; JPCM 2014; (ESA Prodex/PECS contracts) 14.5p

  • IV. Summary: Main research directions
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Molecular scale modeling of nucleation phenomena (HPFC) Modeling of systems of more complex orientation maps Modeling of crystallization in biological systems

16

  • V. Future directions

15p

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Institute for Solid State Physics and Optics WIGNER RESEARCH CENTRE FOR PHYSICS

Hungarian Academy of Sciences H-1121 Budapest, Konkoly-Thege u. 29-33

Computational Materials Science Group in WRCP:

László Gránásy

  • Prof. - team leader nucleation, PF, DFT, …

Tamás Pusztai

  • Sci. Adv..
  • nucleation, PF, topological defects

György Tegze Sen. Sci.

  • CFD, num. methods

Gyula I Tóth Lecturer

  • continuum models

Frigyes Podmaniczky PhD student - DFT, anisotropy, nucleation László Rátkai PhD student - eutectics, LB flow Bálint Korbully PhD student - grain coarsening, top. defects Gyula I.Tóth Lecturer in Appl. Mathematics Loughborough László Gránásy

  • Sci. Advisor

Tamás Pusztai

  • Sci. Advisor

Frigyes Podmaniczky PhD student László Rátkai PhD student György Tegze Senior Scientist Bálint Korbuly PhD student

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15.5p