# Computational Physics

Lecture finished January 2023 | Lecture Notes

## Computational Physics with Focus on Time-Dependent quantum mechanics

Computational Physics | Python |The goal of the course is to learn how to solve complex physical problems by means of computer coding and simulations. Topics include various numerical methods that are used in quantum mechanics, as well as in other fields. The problems will be designed with the focus on quantum-mechanical problems, and especially on quantum-dynamical processes. Examples of algorithms and some problems: Lancos algorithm Monte Carlo algorithm Machine Learning Numerov Algorithm Cranck-Nicolson Algorithm Runge-Kutta algorithm Approximate solutions of the time-dependent Schrödinger equation Calculations of the temporal evolution of a wave function, electron density, spin state, spectra, taking into account decoherence, etc.

Jul 2, 2022 | Project Showcases

## Bachelor Thesis

Computational Physics | DFT | Thesis |In summary, my bachelor project was about a software package used in the research group and how to best run it on our local compute cluster. When I started my bachelor project, the group of Professor Wehling was starting pretty fresh at the university. The project we ended up on was to look at Quantum Espresso, evaluating how it runs best on our local compute cluster Quantum Espresso is a software package implementing Density Functional Theory (DFT), which is a very successful method to calculate electronic properties of materials.