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ABOUT THE COURSE:
Computational Methods in Materials Science and Engineering provides a comprehensive introduction to numerical techniques essential for solving complex problems in materials design. The course covers fundamentals of linear algebra, solutions to linear and nonlinear systems, numerical optimization, and methods for solving ODEs and PDEs using finite difference and spectral approaches for both initial and boundary value problems. Students will explore key algebraic and differential equations used in multiscale modeling, including the many-electron Schrödinger equation and classical molecular dynamics in multi-atom systems. The course also introduces Monte Carlo simulations for modeling diffusion and phase transformations, along with numerical solutions to the Allen-Cahn and Cahn-Hilliard equations, which form the foundation of phase-field modeling for microstructural evolution. Additionally, students will learn optimization techniques for identifying local and global minima in free energy landscapes and other complex scenarios. The course emphasizes hands-on implementation, equipping students with practical skills in Python and C, as well as training in open-source software such as Quantum Espresso, LAMMPS, and MicroSim for real-world computational materials science applications.
INTENDED AUDIENCE: 3rd/4th year BTech and 1st year MTech
PREREQUISITES:Course is meant for UG (3rd and 4th year), PG/PhD (1st year). No pre-requisite for other courses.
INDUSTRY SUPPORT: Tata Steel, GE, Dassault Systemes, Materials Design Inc, Schrodinger, QuesTek Innovation LLC, TCS Engineering Services, Micron, John Deere, ARAI, Mahindra & Mahindra, ANSYS, Multiscale Technologies, Hindalco
Computational Methods in Materials Science and Engineering provides a comprehensive introduction to numerical techniques essential for solving complex problems in materials design. The course covers fundamentals of linear algebra, solutions to linear and nonlinear systems, numerical optimization, and methods for solving ODEs and PDEs using finite difference and spectral approaches for both initial and boundary value problems. Students will explore key algebraic and differential equations used in multiscale modeling, including the many-electron Schrödinger equation and classical molecular dynamics in multi-atom systems. The course also introduces Monte Carlo simulations for modeling diffusion and phase transformations, along with numerical solutions to the Allen-Cahn and Cahn-Hilliard equations, which form the foundation of phase-field modeling for microstructural evolution. Additionally, students will learn optimization techniques for identifying local and global minima in free energy landscapes and other complex scenarios. The course emphasizes hands-on implementation, equipping students with practical skills in Python and C, as well as training in open-source software such as Quantum Espresso, LAMMPS, and MicroSim for real-world computational materials science applications.
INTENDED AUDIENCE: 3rd/4th year BTech and 1st year MTech
PREREQUISITES:Course is meant for UG (3rd and 4th year), PG/PhD (1st year). No pre-requisite for other courses.
INDUSTRY SUPPORT: Tata Steel, GE, Dassault Systemes, Materials Design Inc, Schrodinger, QuesTek Innovation LLC, TCS Engineering Services, Micron, John Deere, ARAI, Mahindra & Mahindra, ANSYS, Multiscale Technologies, Hindalco
