Computational Materials Science

Computational materials science involves computational tools for solving materials related problems. There exist different mathematical models for investigating problems at multiple length and time scales which help in understanding evolution of material structures (at different length scales) and how these structures effectively control material properties. With this understanding we can select materials for specific applications and also design advanced materials for new applications. At electronic level, Density Functional Theory (DFT) is a popular computational tool while Molecular Dynamics (MD) and Monte Carlo (MC) methods are considered as preferred tools for atomistic simulations. Phase-field Method (PFM) is frequently used for materials problems at micron and mesoscale (between micro and nano) regimes. It helps in understanding temporal evolution of microstructures at these length scales. There are also different models related to solid mechanics, transport phenomena etc. for calculation at continuum level. Finite Element Method (FEM) is most useful computational technique for materials related calculation at structural level. There often exist material problems which have important features at multiple length scales. Multiscale modeling technique is often used for such problems. It involves exchange of information between computational tools at different length scales. These models also take input from thermodynamic and kinetic databases for making quantitative predictions.

About This Book

This book covers the essentials of Computational Science and gives tools and techniques to solve materials science problems using molecular dynamics (MD) and first-principles methods. The new edition expands upon the density functional theory (DFT) and how the original DFT has advanced to a more accurate level by GGA+U and hybrid-functional methods. It offers 14 new worked examples in the LAMMPS, Quantum Espresso, VASP and MedeA-VASP programs, including computation of stress-strain behavior of Si-CNT composite, mean-squared displacement (MSD) of ZrO2-Y2O3, band structure and phonon spectra of silicon, and Mo-S battery system. It discusses methods once considered too expensive but that are now cost-effective. New examples also include various post-processed results using VESTA, VMD, VTST, and MedeA.

Syllabus

Part I: Atomistic Methods – Density Functional Theory

1. Why Materials Modelling?

2. Quantum Mechanics & Density Functional Theory (DFT)

3. DFT in practice 

Part II: Atomistic Methods – Classical Approaches

4. Classical Interatomic Potentials

5. Molecular Dynamics & Monte Carlo Simulations

6. The United Atom Method and Coarse Graining 

Part III: Data-driven Methods: Informatics & Machine Learning

7. What is machine learning?

8. Machine learning components: data, fingerprinting, learning

9. Machine Learning in materials science

10. Other advanced methods and materials design

Part IV: Meso-scale & Macro-scale Methods

11. Phase field modelling

12. Computational Thermodynamics

Intro to caliphal and computational thermodynamics

Examples of thermically

Introduction to phase field method

Examples from MEMPHIS – phase field code of Sandia-CINT