Course description

Computational Materials Science utilizes advanced simulations, modeling, and data-driven techniques to study and predict material properties at the atomic and molecular levels. By integrating physics, chemistry, and computer science, it accelerates the discovery and optimization of new materials for applications in energy, electronics, aerospace, and nanotechnology. This field enables cost-effective and efficient material design, reducing the need for extensive experimental trials.

What will i learn?

• Get an idea of the issues and challenges involved in calculations of atomic, molecular and bulk properties of materials and how to approach their resolution using open source software tools.
• Code and execute concepts of Molecular Dynamics, Monte Carlo Methods in Molecular Dynamics and derive thermodynamic properties of materials ensuing from Classical Statistical Mechanics using Python programming language and opens source platforms like Google Colab/ Anaconda with Jupyter Notebook.
• Code and execute concepts of Hartree-Fock Theory and Density Functional Theory using Python (in Google Colab/ Anaconda & Jupyter Notebook environments) and derive various molecular and bulk material properties ensuing from electronic structure calculations involving Quantum Mechanics and Quantum Statistics.
• Use open source software like Quantum Espresso or BIOVIA Material Studio for designing, analysing and visualization of various types of materials and their properties.