University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Instructor: Leonid Zhigilei
Office: Wilsdorf Hall 303D
Office Hours: open
Telephone: (434) 243 3582E-mail: [email protected]
Class web page: http://www.people.virginia.edu/~lz2n/mse627/
Class e-mail list: [email protected]
Contact Information:
Spring 2018, Tuesday and Thursday, 2:00 - 3:15 pmThornton Hall D115
MSE 4270/6270: Introduction to Atomistic Simulations
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Research in Computational Materials Group:
nanofibrous and nanocrystalline materials
crystal defects, phase transformations:
Group Web Site: http://faculty.virginia.edu/CompMat/
laser-materials interactions: nano-structured materials:
Development of computational methods for materials modeling at multiple length & time-scales
Investigation of non-equilibrium materials processing, properties of nanostructured materials,mechanisms of phase transformations
acoustic activation of surface processes:
acoustic pulse
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Term project 50% Homework 40% Presentation/discussion of research articles 10%
Grading:
Homework - cooperation among students is permitted. Discussionsthrough the course e-mail list are especially encouraged.
Each student will lead one discussion of a research paper in the area ofatomistic simulations (10 min). You can propose a paper that looksinteresting to you or is relevant to your research work (but not to yourterm project).
Discussion of published research articles:
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Term project:
Objective: To get experience in designing and performing computersimulations
Parts of the project:
Choose a problem that is of scientific or computational interest to you.
Choose an appropriate computational approach, justify your choice.
Write a computer code (or add parts to MSE627-MD or MSE627-MCcodes).
Perform simulations and analyze the results.
Prepare a report/paper. Use format typical for a research paper, withabstract, introduction, computational setup, results, discussion, conclusions,references. Figures and figure captions should be included in the text.
Make a presentation to the class at a mini-symposium that substitutesthe final exam.
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Term project:
Timeline:
February 1st – decide in the topic/title of your project and inform theinstructor
March 1st – prepare the first draft of the introduction (with referencesto relevant papers) and discuss progress with instructor (optional)
May 5th and 6th (tentative dates) – turn in a report; give a presentationto the class at a mini-symposium
A problem chosen for the term project should have some science contentand be doable in the timeframe of one semester.
If the intention is to continue computational work in the future, the termproject may be a well-defined part of a larger research project.
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Textbooks:
Online Version is available through UVa library: M. P. Allen and D. J. Tildesley, Computer simulation of liquids (Oxford
Scholarship Online: 2017). D. Frenkel and B. Smit, Understanding molecular simulation: from
algorithms to applications (Academic Press: San Diego, 1996).
Books placed on reserve circulate: M. P. Allen and D.J. Tildesley, Computer simulation of liquids (Clarendon
Press: Oxford, 1990, 1987). D. Frenkel and B. Smit, Understanding molecular simulation: from
algorithms to applications (Academic Press: San Diego, 1996). M. Metcalf and J. Reid, Fortran 90/95 explained (Oxford University Press:
Oxford, New York, 1999) – two copies.
Additional books that may be useful are listed athttp://www.people.virginia.edu/~lz2n/mse627/mse627-books.html
Main text: Handouts and lecture notes.
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
“The underlying physical laws necessary for the mathematical theory of a largepart of physics and the whole of chemistry are thus completely known, and thedifficulty is only that the exact application of these laws leads to equationsmuch too complicated to be soluble.” – Dirac, 1929.
The equations that describe quantum mechanics, classical mechanics, gas/fluidflow, electrical/magnetic fields induced by static or moving charges, are wellknown. But analytical solutions are often intractable.
The invention of computers has provided a new exciting direction – to solve thecomplex equations numerically, in a computer simulation.
Experiments - mathematical description - computer simulation
Once a certain number of experiments have been performed, it is necessary todescribe the results mathematically. If we succeed in describing thephenomenon with mathematical equations, then we can predict the behavior ofthe system of interest for a wide range of conditions, including the ones forwhich experiments are difficult, too expensive, or not possible at all.
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
What are the classical “atomistic simulations”?
Materials Science
Physics
ChemistryComputer ScienceMathematics
MechanicalEngineering
Atomistic Simulations
Basic units of the model – atoms/molecules But… the same methods are often applicable to systems consisting of other types of discrete particles, e.g. granules, particles in fluids, planets, etc.
Atomistic simulations are different from (a) continuum simulations operating with “fields” – quantity is defined at every point in space; (b) electronic structure calculation performed for fixed positions of nuclei.
Interdisciplinary nature of “atomistic simulations”
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
“Classical” Materials Science: analysis of individual defects, connectionbetween the microstructure and properties of materials, formation ofmicrostructure in processing, mechanisms of phase transformations…
Chemistry and Biochemistry: nature of interatomic bonding, reactions,vibrational relaxation and energy transfer, molecular structures, drug design,structure of membranes, protein folding, …
Mechanical Engineering: large-scale problems are typically solved at continuumlevel, where the atomic-level details are incorporated through averaging andformulation of constitutive laws. Atomistic simulations are used for conditionswhen continuum constitutive laws fail, e.g. large gradients, flows far fromequilibrium, high/intermediate Knudsen number flows, heat transfer, mechanicalproperties, and friction at nanoscale…
Statistical Mechanics, Physics: theory of liquids, correlated many-bodymotion, properties of statistical ensembles, structure of small clusters …
Computer Science and Mathematics: Numerical algorithms, analyticalmethods, development of computational tools.
Interdisciplinary nature of “atomistic simulations”
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Nano-scale: Characteristic length ~10-9 - 10-7 m. Characteristic times ~10-14 -10-10 s. Atomic level, properties of individual defects (dislocations, vacancies,interstitials, dopants), defect mobility, diffusion, clusters, surface reactions.
Micro-scale: Characteristic length ~10-8 - 10-6 m. Characteristic times ~10-11 –10-8 s. Small ensembles of lattice defects at length scale below the grain size,defect interactions, precipitates, dislocation reactions, microcrack nucleation.
Meso-scale: Characteristic length ~10-7 - 10-4 m. Characteristic times ~10-9 -10-3 s. Ensembles of lattice defects at length scale of the grain size, shearbands, dislocation walls, disclinations, collective dynamics of microstructure,interface diffusion, grain coarsening, recrystallization, crack growth, fracture.
Macro-scale: Characteristic length 10-3 m. Characteristic times 10-3 s.Sample geometry, mechanics, plasticity of polycrystalline materials,temperature fields, hydrodynamic motion, microstructure homogenization etc.
Structural hierarchy, characteristic length- and time-scales
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigileiquantum nano micro meso macro
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structure of diamond surface Zhigilei, Srivastava & Garrison
crack propagation, F. F. Abraham, IBM
Nanocrystalline material, M. Li, JHU plastic deformation of a crystalhttp://zig.onera.fr/DisGallery/index.html
Computational methods in materials science
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
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quantum nano micro meso macro
Ab initio MD, Quantum MC
Classical MD, Metropolis MC
Kinetic MC (up to 108 atoms)
Dislocation Dynamics: early stages of plastic deformation
MC Potts Model: grain structures in polycrystalline materials
Phase Field Models: solidification, phase transformations…
Coarse-Grained Models: molecular systems, polymers…
Finite element or finite differences methodsare used to solve a system of PDE.
Continuum, constitutive relations
Mesoscopic models
Microscopic/atomistic models
general
System-specific
Computational methods
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Length and time scales in materials modeling
by Greg Odegard, NASA
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
An emerging understanding of the connections between the structureand properties of materials has lead to a remarkable progress in thedesign of new advanced materials.
from M. A. White, Properties of Materials
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Computational methods in materials science
by Dierk Raabe
# of atoms
109
103
1024
billions of atoms
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Particle and continuum computational models
by W. Dzwinel
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Multiscale modeling of dislocations in semiconductors
by V. Bulatov, LLNL
TMS report, 2015
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Plan of the course
Numerical integration of differential equations (ODE for particle dynamics).– hands on
Atomistic methods - Molecular dynamics, kinetic and Metropolis MonteCarlo. Computer codes will be provided. The knowledge from this part ofthe course should be sufficient to design and perform your own simulations.– hands on
Advanced techniques in atomistic simulation that may be useful in yourresearch projects. Control of temperature and pressure. Methods for analysisof simulation results, diffusion, spatial and time correlation functionsReview of interatomic potentials.
Mesoscopic techniques (Dislocation Dynamics, Kinetic MC, Potts model,Cellular automata etc.) – overview/examples (time permitting)
Multiscale approaches (hierarchical and combined). Coupling of continuumand atomistic models. – brief overview (time permitting)
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Computational molecular nanotechnology at NASA
Carbon nanotubes are considered as potentialbuilding block in future nanoscale materials,sensors, machines, and computers.
by Deepak Srivastava, NASA Ames
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Large-scale simulation of crack propagation
Molecular dynamics simulation with ~1 billion atoms illustrates crackpropagation in a ductile metal. At first, the crack moves very rapidly and localbonds break in a "brittle" manner, but at some point the crack-tip begins to emitdislocations (the tangles in the picture) and stops propagating. Such a crack issaid to become blunted and begins to cause intense local deformation but notfailure.
by F. F. Abraham et al.
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Deformation of nanocrystalline aluminium
Four grains 20 and 70nm in size (97,000 and1,021,000 atoms) aresimulated.
V. Yamakov, D. Wolf, S.R. Phillpot, A. K. Mukherjee, and H. Gleiter, Nature Materials 1, 45–49 (2002).
Mechanical twinning plays an important role in plastic deformation of nanocrystalline Al.
MD simulations with EAM - type potential for Alwere carried out under constant tensile loads of2.3–2.5 GPa and at a temperature of T = 300 K.
A movie from the simulation can be found athttp://www.nature.com/nmat/journal/v1/n1/extref/nmat700-s1.mov
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Nanodroplet Activated Folding of Graphene
Patra, Wang, Král, Nano Lett. 9, 3766 (2009)
Molecular dynamics simulations of spontaneous folding of atomically thin layers of graphite (graphene sheets) induced by droplets of water
nano-origami
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Collision of a droplet with a substrate
Target
Emitter voltage (+8 kV)
Extractor voltage (+4 kV)
Mass analyzer entrance (+10V)
Target voltage (+30V)
3). Analyte ions
release in the
gas phase
1) Analyte dissolved in the electrolyte solution for electrospray
2) Clusters are destroyed by the impact
Grid
MD simulation by Yasushi Katsumi (term project for MSE 4270)
Experiment: Sergei Aksyonov and Peter Williams (Impact Desolvation of Electrosprayed Clusters (IDEC)
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Collision of a droplet with a substrate
MD simulation by Yasushi Katsumi (term project for MSE 4270)
Impact velocity is 2000 m/s
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Collision of a droplet with a substrate
MD simulation by Yasushi Katsumi (term project for MSE 4270)
Impact velocity is 500 m/s
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Growth of fractal structures in fullerene layers
Monte Carlo simulation by Hui Liu (term project for MSE 6270)
STM images of C60 film growing on graphite
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
“Big picture” of laser spallation and ablation from atomistic simulations
mechanisms of ablation & spallation
“mosaic” approach to mapping the processes occurring at the scale of the whole laser spot
surface morphology
Ionin et al., JETP Lett. 94, 753, 2011
Appl. Phys. A 114, 11, 2014
Vorobyev and GuoPRB 72, 195422, 2005
Al target 150 ps after
irradiation by a 100 fs pulse
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Modeling of matrix-assisted pulsed laser evaporation (MAPLE)
MAPLE is used for “gentle” ejection & deposition of thin polymer and nanocomposite films
Polymer molecules & CNTs are deposited on
a substrate
Volatile solvent molecules are pumped away
Frozen solution of polymer molecules and other nanoscale elements, e.g. CNT
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: MD simulation of the ejection of carbon nanotubes in MAPLE
More animations can be found athttp://www.faculty.virginia.edu/CompMat/maple/
Large scale coarse-grained MD simulation (~20 millions of matrix molecules + more than a thousand of CNTs)
This animation shows the ejection of carbon nanotubes (CNTs) from a target where a network of interconnected CNT bundles is embedded into a solvent that strongly absorbs laser light.
Short pulse laser irradiation results in the explosive boiling of the solvent and ejection of large tangles of CNT bundles (up to 50 MDa in mass).
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: MD simulation of Rayleigh-Bénard phenomenon
D. C. Rapaport, J. Phys.: Condens. Matter. 26, 503104, 2014
Streamlines from MD simulation of Rayleigh–Bénard flow pattern produced by convection in a fluid layer heated from below.
color coded streamlines show temperature variation
Simulations is done for 107 particles
nascent flow state after 4 × 104 timesteps
well-ordered cell array after 107 timesteps
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Examples: Dislocation Dynamics Method
A nice collection of animations from dislocation dynamics simulations can be found at http://zig.onera.fr/DisGallery/index.html
One of the main mechanisms for dislocationmultiplication under stress is the Frank-Readmill or Frank-Read source. The operation of aFrank-Read source can be observed on adislocation segment pinned at its ends.
Dislocation dynamics during deformation ofa FCC single crystal (Cu) with lineardimension of 15 micrometers.
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Mesoscopic modeling of carbon nanotube materials
Structure of CNT network materials
0.005 g/cm3 0.025 g/cm3
density
homogeneous poorly connected network
heterogeneous “star” structure, large pores
spontaneous formation of bundles in a thin CNT film
500 500 20 nm3 sample, density 0.2 g/cm3, ~1500
(10,10) CNTs of 200 nm length
Periodic boundary conditions in the plane of the film
Color of nanotubes corresponds to their local inter-tube interaction energy
Heat transfer in CNT materials
Phys. Rev. B, 71, 165417, 2005Phys. Rev. Lett. 104, 215902, 2010ACS Nano 4, 6187, 2010Appl. Phys. Lett. 101, 043113, 2012
University of Virginia, MSE 4270/6270: Introduction to Atomistic Simulations, Leonid Zhigilei
Optional Reading
R. LeSar and D. C. Chrzan, Is computational materials science overrated? Materials Today 2, 21-23, 1999.
R. Gomperts, E. Renner, and M. Mehta, Enabling technologies for innovative new materials simulations, American Laboratory 37, 12-14 2005.
U. Landman, Materials by numbers: Computations as tools of discovery, PNAS 102, 6671-6678, 2005. [read 1st and last 2 pages]
NSF White Paper on “Matter by design,“ 2011.
White House Materials Genome Initiative, 2011.
Report on Mesoscale Science from the DOE Basic Energy Sciences Advisory Committee, 2012.
Overview of modeling projects funded by EU FP7 in 2007-13.
A roadmapping study for connecting materials models and simulations across length and time scales, TMS and NIST, 2015.
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