Syllabus
- Discrete problems (combinatorics, enumeration, lattice problems, prediction of NMR and EPR spectra)
- Continuous problems (root finding, minimization, integration, interpolation)
- Problems described by ordinary differential equations (chemical kinetics, molecular mechanics)
- Problems described by partial differential equations (Poisson-Boltzmann equation, diffusion, Schrödinger equation)
- Electronic structure of atoms and molecules (Hartree-Fock method, configurational interaction, density functional theory, quantum Monte Carlo methods)
- Group theory and symmetry (applications to electronic structure and spectroscopy)
- Statistical description of data and modeling (least squares methods and confidence limits, estimation of statistical parameters)
- Statistical thermodynamics (ensembles, Monte Carlo methods, molecular dynamics, free energy calculation)
- Chemoinformatics (quantitative structure-activity relationship, molecular descriptors)
- Machine learning in chemistry (supervised learning, linear models, kernels, feature sets, neural networks)
- Signal processing and discrete Fourier transform
Annotation
The course Theoretical Methods in Chemistry provides an overview of basic techniques that are common in different fields of theoretical chemistry (such as quantum and computational chemistry, chemical kinetics, chemical and statistical thermodynamics, chemoinformatics, molecular modeling). After explaining the theoretical background of major topics (such as electronic structure of atoms and molecules, ensembles in statistical thermodynamics, reaction rates in chemical kinetics, …) the course describes how the resulting problems can be solved mostly using numerical methods on a computer.
The course is supplemented by a practical workshop.