MATERIALS SCIENCE -

(COMPUTING & EXPERIMENTAL):

I would say that the scope of computational materials science is to model/predict the behavior of materials based on their composition, micro-structure, process history, and interactions. It is a complex problem with many variables. Ideally, you would build the model from the ground up (atom-by-atom) but this scales miserably in terms of computational power required and so you try to use models which aggregate properties into larger chunks - but this then requires more assumptions and limits the models in some ways - it may work well for some materials and problems but not for others so the assumptions become very important. This is inherently a problem of scale - you need to model a material which can be done well at a nanoscopic scale but need to be simulated at a macroscopic scale to be believable for applications. This becomes computationally limited and then you are using computational tricks to speed up the simulation. The other approach is to use clever mathematics and modeling to help - I think density functional theory (one of the creators won a Nobel Prize for this about 20 years ago) is something that helps with this. I would encourage people to in the field to correct me if I am mistaken in this explanation.

An experimental knowledge is also needed to support the theoretical prediction of nanomaterials is necessary and thus a requirement in knowing about characterisation techniques too!!

Course content to be covered:

Basic Materials Science:

Structure of solids, symmetry concepts, crystal structure. Preparative methods and characterization of inorganic solids. Crystal defects and non-stoichiometry. Interpretation of phase diagrams, phase transitions. Kinetics of phase transformations, structure property correlations in ceramics, glasses, polymers. Composites and nano-materials. Basics of magnetic, electrical, optical, thermal and mechanical properties of solids

References:

1. West, A. R. 1984 Solid State Chemistry and its Applications, John Wiley and Sons.

2. Shackelford, J. F. 1988 Introduction to Materials Science for Engineers, MacMillan.

3. P. Raghavan, Materials Science and Engineering.

Nanoscience and Nanotechnology:

Introduction to Nanotechnology, Synthesis methodologies, Various kinds of nanostructures, Physical properties of nanomaterials

References:

1. Guozhong Cao, Nanostructures and Nanomaterials : Synthesis, Properties and Applications, Imperial College Press 2004.

2. T. Pradeep, Nano: The Essentials - Understanding nanoscience and nanotechnology, Tata Mc Graw Hill Publishing Company Limited, New Delhi, 2007.

3. Nanomaterials Synthesis, Properties and Applications Edited by A S Edelstein and R C Cammarata, IOP Publishing Ltd 1996.

4. T. Pradeep, Introduction to Nanoscience & Nanotechnology, TMH.

Nanomaterials- Characterisation and Properties:

Classification of nanomaterials, Crystalline nanomaterials and defects, Multiscale hierarchical structures built out of nanosized building blocks, Nanomaterials in Nature: Nacre, Gecko, Teeth; Nanostructures: Carbon Nanotubes, Fullerenes, Nanowires, Quantum Dots. Applications of nanostructures. Reinforcement in Ceramics, Drug delivery, Giant magnetoresistance, etc. Cells response to Nanostructures; Surfaces and interfaces in nanostructures. Ceramic interfaces, Superhydrophobic surfaces, Grain boundaries in Nanocrystalline materials, Defects associated with interfaces; Thermodynamics of Nanomaterials; Overview of properties of nanostructures and nanomaterials. How the performance of nanomaterials come about: size-structure-Mechanism-property-performance pathway; Overview of characterization of nanostructures and nanomaterials; Deformation behaviour of nanomaterials. Fracture and creep. Nanomechanics and nanotribology; Electrical, Magnetic and Optical properties.

Focus on: Brunauer-Emmett-Teller (BET) technique, Transmission Electron Microscopic techniques, Auger Electron Spectroscopy, Xray Photoelectron Spectroscopy. Electron Energy Loss Spectroscopy.

References:

1. Nanomaterials, Nanotechnologies and Design: an Introduction to Engineers and Architects, D. Michael Ashby, Paulo Ferreira, Daniel L. Schodek, Butterworth-Heinemann, 2009.

2. Handbook of Nanophase and Nanostructured Materials (in four volumes), Eds: Z.L. Wang, Y. Liu, Z. Zhang, Kluwer Academic/Plenum Publishers, 2003.

3. Handbook of Nanoceramics and their Based Nanodevices (Vol. 2) Edited by Tseung-Yuen Tseng and Hari Singh Nalwa, American Scientific Publishers.

Computational Mathematics:

Vector and tensor algebra; Basics of linear algebra and matrix inversion methods; Coordinate transformations methods; Optimization methods, Probability and statistics; Numerical methods: Concepts of discretization in space/time, implicit, explicit; Solution to ODEs(Euler, Heun, Runge-Kutta methods), PDEs (Elliptic, Parabolic, Hyperbolic), solutions to Laplace equation and applications, transient diffusion and wave equation; Discretization methods (FDM, FVM, FEM); iterative solution schemes Jacobi, Gauss-Seidel, ADI, Multigrid, Fourier-spectral schemes; Root finding methods, interpolation, curve-fitting, regression; Special functions: Bessel, Legendre, Fourier, Laguerre, etc.,

Computational tools for the solution to all the above problems will be discussed along with canonical examples from materials problems.

Resources in MATLAB, Python and FORTRAN are used.

Resources:

1. Advanced Engineering Mathematics by Erwin Kreyzig.

2. Mathematical Physics by V. Balakrishnan.

3. Numerical Methods for Engineers by Steven C. Chapra and Raymond P. Canale.

4. Numerical Recipes in C by William H Press, Vetterling, Teutolsky and Flannery.

Modelling and Simulation:

Importance of modeling and simulation in Materials Engineering. nd numerical approaches. Numerical solution of ODEs and PDEs, explicit and implicit methods, Concept of diffusion, phase field technique, modelling of diffusive coupled phase transformations, spinodal decomposition. Level Set methods, Celula Automata,: simple models for simulating microstructure,. Finite element modelling,: Examples in 1D, variational approach, interpolation functions for simple geometries, (rectangular and triangular elements); Atomistic modelling techniques,: Molecular and Monte-Carlo Methods.

References:

1. A.B. Shiflet & G.W. Shiflet: Introduction to Computational Science: Modelling and Simulation for Sciences, Princeton University Press, 2006.

2. D.C. Rapaport: The Art of Molecular Dynamics Simulation, Cambridge University Press, 1995.

3. K. Binder & D.W. Heermann: Monte Carlo Simulation in Statistical Physics, Springer, 1997.

4. K.G.F. Jansenns, D. Raabe, E. Kozeschnik, M.A. Miodownik, B. Nestler: Computational Materials Engineering: An Introduction to Microstructure Evolution, Elsevier Academic Press, 2007.

5. David V. Hutton: Fundamentals of Finite Element Analysis

Computational Modelling of Materials:

Introduction to computational modeling of materials, description of atomic interaction, tight binding approximation, Hartree-Fock, molecular orbital method, density functional theory. Applications of these methods in modeling of mechanical, electronic, magnetic, optical, and dielectric properties of materials, design principles of novel materials.

References:

1. Richard Martin: Electronic Structure: Basic Theory and Practical Methods, Cambridge.

Resources recommended during work:

Fundamentals: Prof. Tadmor's book "Modeling Materials" (Electrodynamics from Griffiths & Jackson )

Mechanical & Thermal Properties: Prof. Rob Phillips "Crystals Defects and Microstructures"

Optical, Electrical & Magnetic properties: "Solid State Physics" by Aschcroft & Mermin

Real life applications: Integrated Computational Materials Engineering

Modelling: Basic C++ and Python along with numerical analysis ("Numerical Analysis of Differential Equations by Arieh Iserles" )

Materials Handbook: "Springer Handbook of Condensed Matter and Materials Data"

Software database recommended:

Modelling Materials Nano Hub Materials Project

Programming Languages & Skills used:

Python, Fortran, UNIX, Machine Learning, MATLAB, Mathematica.

Common Computational Software Used:

Indeed VASP (Vienna Ab initio Simulation Package) is highly recommended but it is a paid software, thus alternatives are recommended as below given:

Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA)

This resource has access to SIESTA - GitHub (or) GitLab webpage. [Needs to have proper access to LINUX interface]

Density Functional based Tight Binding (and more) (DFTB+)

This can be directly installed as an executable in LINUX or can be used via Anaconda (Read Docs/DFTB+ recipes)

General Atomic and Molecular Electronic Structure System (GAMESS)

This can be used on LINUX and Windows as well, but LINUX is most preferred. For this you need to apply for access to GAMESS admin by a form, which will be approved via email and details will be shared.

Semiempirical Tight Binding (XTB)

This can be directly installed as an executable in LINUX or can be used via Anaconda (Read xtb Docs).

Avogadro

Avogadro is an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.

A useful tip:

• The common problem I have observed with many users is that they use Windows OS installed on their PC (people who directly use LINUX or Mac OS will not face any issues). For them a best solution to use Windows OS along with LINUX is via WSL2 (Windows Sub-system for LINUX).

• Make sure you work accordingly with the core present (no. of cores and type of core) in your PC don't stress the PC to work out of it's capabilities.

• This is just an solution for smaller calculations, mostly try to connect to the possible clusters present in a research lab or university for huge calculations (as most of them have supercomputer facility or cluster and server based access (High-Performance Computing facility))

For Windows 10 OS-WSL2 Setup: Ubuntu-WSL2-W10

For Windows 11 OS-WSL2 Setup: Ubuntu-WSL2-W11