Module #1 Introduction to Advanced Computational Chemistry Overview of computational chemistry, importance of advanced methods, and course objectives
Module #2 Quantum Mechanics Review Review of quantum mechanics principles, wave functions, and operators
Module #3 Density Functional Theory (DFT) Fundamentals Introduction to DFT, Hohenberg-Kohn theorems, and exchange-correlation functionals
Module #4 DFT Applications:Electronic Structure and Properties Computing electronic structure, band gaps, and optical properties using DFT
Module #5 Post-Hartree-Fock Methods:MP2 and CCSD(T) Introduction to post-Hartree-Fock methods, MP2, and CCSD(T) for accurate energy calculations
Module #6 Coupled-Cluster Theory and its Applications Coupled-cluster theory, singles, doubles, and perturbative triples (CCSD(T)) methods
Module #7 Solvent Effects and implicit Solvation Models Methods for including solvent effects, implicit solvation models, and continuum solvation models
Module #8 Free Energy Calculations:Thermodynamic Integration and FEP Computing free energy differences using thermodynamic integration and free energy perturbation methods
Module #9 Molecular Dynamics Simulations:Basics and Applications Introduction to molecular dynamics, integration algorithms, and applications to chemical systems
Module #10 Enhanced Sampling Methods:Replica Exchange and Metadynamics Accelerating molecular dynamics simulations using replica exchange and metadynamics
Module #11 Monte Carlo Methods for Chemical Systems Introduction to Monte Carlo methods, Markov chain Monte Carlo, and applications to chemical systems
Module #12 Machine Learning in Computational Chemistry Introduction to machine learning, neural networks, and applications to chemical systems
Module #13 Quantum Machine Learning and Hybrid Approaches Quantum machine learning, hybrid classical-quantum approaches, and applications to chemical systems
Module #14 Computational Electrochemistry:Batteries and Fuel Cells Computational methods for electrochemical systems, batteries, and fuel cells
Module #15 Catalysis and Reaction Mechanisms Computational methods for understanding catalysis, reaction mechanisms, and transition states
Module #16 Materials Science and Nanoscale Systems Computational methods for understanding materials properties, nanoscale systems, and defects
Module #17 Biomolecular Systems and Simulation Methods Computational methods for understanding biomolecular systems, protein-ligand interactions, and simulation methods
Module #18 Computational Pharmacology and Drug Design Computational methods for understanding drug-target interactions, pharmacokinetics, and drug design
Module #19 High-Performance Computing and Parallelization Introduction to high-performance computing, parallelization strategies, and optimization techniques
Module #20 Best Practices for Computational Chemistry Research Best practices for computational chemistry research, reproducibility, and standardized reporting
Module #21 Case Studies in Advanced Computational Chemistry Real-world applications and case studies of advanced computational chemistry methods
Module #22 Current Challenges and Future Directions Current challenges and future directions in advanced computational chemistry methods and applications
Module #23 Software and Tools for Advanced Computational Chemistry Overview of software and tools for advanced computational chemistry, including commercial and open-source options
Module #24 Course Wrap-Up & Conclusion Planning next steps in Advanced Computational Chemistry Methods career
