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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, statistical thermodynamics, molecular modeling, chemoinformatics, …). After explaining the theoretical background (such as electronic structure of atoms and molecules, ensembles in statistical thermodynamics, …) the course describes how the resulting equations can be solved using numerical methods on a computer. The course is supplemented by a practical workshop.
Poslední úprava: Uhlík Filip, prof. RNDr., Ph.D. (21.12.2025)
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- P. W. Atkins, J. de Paula, J. Keeler: Atkins’ Physical Chemistry, Oxford University Press, 2018, ISBN 0198769865. Poslední úprava: Uhlík Filip, prof. RNDr., Ph.D. (21.12.2025)
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Final mark is based on the final exam (66%) and credit derived from class participation during course (33%). Final exam is comprised equally of a (1 hr) written exam and oral exam, to cover the concepts discussed within the course materials. Poslední úprava: Heard Christopher James, Ph.D. (05.09.2024)
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Lecture 1: Introduction to theoretical chemistry History of chemical theory, development of theoeretical methods in chemistry (quantum chemistry). Development of computational techniques. Introduction to electronic structure theory Concepts in modelling (accuracy/precision) Overview of course structure Lecture 2: Potential Energy Surfaces Concept of PES, harmonic approximation, Born-Oppenheimer Approximation, Normal Mode analysis. Critical points on the PES, algorithms for locating minima/transition states Searching the PES (global optimization and statistical methods for characterising the PES) Classification/visualization of PES Failure of BOA and implications to chemistry/physics/biology Lecture 3: Wavefunction methods in quantum chemistry Single electron methods (Hartree Fock) Variational principle LCAO, slater determinants and basis sets Approximations, derivations and application of HF. Restricted/unrestricted HF Accuracy and limitations - implications Lecture 4: Density methods in quantum chemistry Density-based methods (Thomas-Fermi -> density functional theory) Accuracy and limitations exchange and correlation Linear response Applications Lecture 5: Semi-Empirical methods Force-field methods (LJ, metallic fields, biological/protein FF) Introduction to fitted forcefields (training and testing) Approximate solutions to HF (INDO/MINDO) Huckel theory for conjugated organic molcules Coarse graining for polymers Lecture 6: Post-HF wavefunction methods Correlation methods (CI, perturbation theory, CCSD) Lecture 7: Symmetry and spectroscopy group theory in chemistry Application of symmetry to analysis of bonding, structure and spectral properties Lecture 8: Excited states Failures of BOA, conical intersections, avoided crossings Time-dependent DFT for calculation of optical response, UV-VIS spectra, photoelectron spectra Non-adiabatic dynamics Lecture 9 + Lecture 10: Statistical thermodynamics and molecular simulations (additional materials at https://11c.cz/st) Statistical mechanics Ensembles Partition functions Monte Carlo methods Molecular dynamics Lecture 11 + Lecture 12: Machine Learning Methods Supervised and unsupervised learning Clustering and characterisation (SVM) Multivariate regression (LASSO, KRR) chemoinformatics/materials - LLMs, QSAR Machine Learned Potentials (Featurization, training, active learning, delta learning, error estimation) interpretable AI ML integration into tools (surrogate methods) Poslední úprava: Uhlík Filip, prof. RNDr., Ph.D. (20.02.2026)
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Lecture 1: Introduction to Theoretical Chemistry By the end of this lecture, students will be able to: Trace the historical development of theoretical methods and computational techniques in chemistry. Outline the foundational concepts of electronic structure theory. Distinguish between accuracy and precision within the context of chemical modeling. Navigate the overall structure and expectations of the course. Lecture 2: Potential Energy Surfaces (PES) By the end of this lecture, students will be able to: Define the concept of a Potential Energy Surface (PES) and apply the harmonic and Born-Oppenheimer approximations (BOA). Perform Normal Mode analysis to evaluate molecular vibrations. Identify and locate critical points on a PES, distinguishing between global/local minima and transition states. Evaluate the statistical methods and algorithms used to search and characterize a PES. Explain the implications to chemistry, physics, and biology when the Born-Oppenheimer Approximation fails. Lecture 3: Wavefunction Methods in Quantum Chemistry By the end of this lecture, students will be able to: Explain the principles and derivations of single-electron methods, specifically Hartree-Fock (HF) theory. Apply the variational principle to quantum chemical problems. Construct Slater determinants and explain the Linear Combination of Atomic Orbitals (LCAO) approach and the use of basis sets. Differentiate between restricted and unrestricted HF methods. Assess the accuracy, limitations, and practical implications of utilizing HF methods. Lecture 4: Density Methods in Quantum Chemistry By the end of this lecture, students will be able to: Summarize the evolution of density-based methods from the Thomas-Fermi model to modern Density Functional Theory (DFT). Explain the role and impact of exchange and correlation functionals. Apply linear response theory within the context of DFT. Evaluate the accuracy, limitations, and specific applications of density methods. Lecture 5: Semi-Empirical Methods By the end of this lecture, students will be able to: Describe the formulation of force-field methods, including Lennard-Jones potentials, metallic fields, and biological/protein force fields. Outline the process of training, testing, and fitting force fields. Compare approximate semi-empirical solutions to HF theory (such as INDO and MINDO). Apply Hückel theory to analyze conjugated organic molecules. Explain the principles and utility of coarse-graining for polymers. Lecture 6: Post-HF Wavefunction Methods By the end of this lecture, students will be able to: Differentiate between various electron correlation methods, including Configuration Interaction (CI), perturbation theory, and Coupled-Cluster (CCSD). Evaluate how post-HF methods improve upon standard Hartree-Fock calculations. Lecture 7: Symmetry and Spectroscopy By the end of this lecture, students will be able to: Apply the principles of group theory to chemical systems. Analyze chemical bonding, molecular structures, and spectral properties using symmetry rules. Lecture 8: Excited States By the end of this lecture, students will be able to: Analyze complex excited-state phenomena, including conical intersections and avoided crossings. Calculate optical responses, UV-VIS spectra, and photoelectron spectra using Time-Dependent DFT (TD-DFT). Explain the principles of non-adiabatic dynamics in systems where the BOA fails. Lectures 9 & 10: Statistical Thermodynamics and Molecular Simulations By the end of these lectures, students will be able to: Define core principles of statistical mechanics and differentiate between various statistical ensembles. Calculate and interpret partition functions for chemical systems. Use and understand Monte Carlo calculations for calculation of integrals and sampling of arbitrary distributions. Compare the theoretical foundations and practical applications of Monte Carlo methods and Molecular Dynamics simulations. Lectures 11 & 12: Machine Learning Methods By the end of these lectures, students will be able to: Distinguish between supervised and unsupervised machine learning methods. Apply clustering techniques (like SVM) and multivariate regression models (like LASSO and KRR) to chemical data. Discuss the role of Large Language Models (LLMs) and Quantitative Structure-Activity Relationships (QSAR) in chemoinformatics and materials science. Outline the development of Machine Learned Potentials, including featurization, training, active/delta learning, and error estimation. Evaluate the importance of interpretable AI and the integration of ML as surrogate methods in chemical tools. Poslední úprava: Uhlík Filip, prof. RNDr., Ph.D. (20.02.2026)
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