Computational Chemistry Theory
Introduction
This series of courses covers quantum chemistry and density functional theory, molecular dynamics and machine learning molecular dynamics, solid electronic structure, statistical thermodynamics, and free energy calculation methods. It systematically explores the core principles and cutting-edge applications of computational chemistry.
Course List
- Quantum Chemistry and Density Functional Theory
This course focuses on systematically exploring key methods of quantum chemistry in solving microscopic chemical problems. It covers the basic principles of density functional theory (Density Functional Theory, DFT), as well as the principles and applications of pseudopotentials (Pseudo Potential) and basis sets (Basis Set) in computational chemistry. It aims to establish a solid theoretical foundation for researchers entering the field of computational chemistry.
The first part starts from the development of quantum mechanics, introducing fundamental principles such as wave functions, operators, eigenvalue equations, Schrödinger equations, and the Born-Oppenheimer approximation. It delves into the treatment of many-electron problems, emphasizing their central role in practical chemical systems, and introduces theoretical concepts such as single-electron approximations, the Pauli exclusion principle, and antisymmetric wave functions. Taking the Hartree-Fock (HF) method as an example, it details methods for treating many-electron system wave functions, and discusses electron correlation problems to introduce density functional theory.
The second part systematically expounds on the concepts of electron density and functionals, Hohenberg-Kohn theorems I & II, and the Kohn-Sham equation. It focuses on explaining the physical meaning of various quantities in the Kohn-Sham equation and methods for solving this equation. Finally, it discusses in detail the basic principles and characteristics of exchange-correlation functional methods such as LDA/GGA/meta-GGA/hybrid functionals.
The third part, in conjunction with the practical need for approximate treatment of complex problems in computational chemistry, introduces the principles and applications of pseudopotentials and basis sets. It focuses on discussing the physical meaning of pseudopotentials, as well as the principles of atomic orbital linear combination representation (Linear Combination of Atomic Orbitals, LCAO), polarization functions (Polarisation Functions), and diffuse functions (Diffusion Functions) in basis sets. - Molecular Dynamics/Machine Learning Molecular Dynamics
This course aims to introduce the basic principles of classical molecular dynamics (Classical Molecular Dynamics) and machine learning molecular dynamics (Machine Learning Molecular Dynamics). The course introduces how to use molecular dynamics simulations to study the physicochemical properties of materials and molecules, and understand how to use machine learning methods to generate potential functions to improve the efficiency and accuracy of molecular dynamics simulations. The classical molecular dynamics section covers the basic principles of molecular dynamics, atomic interatomic potentials and classical force fields, numerical solutions of Newton's equations of motion, concepts of statistical ensembles, temperature and pressure control methods, and the implementation process of molecular dynamics simulations. The machine learning molecular dynamics section covers the relationship between machine learning potentials and classical mechanics and quantum mechanics, the principles of machine learning molecular dynamics, the concepts of atomic energy and deep potential smooth models, and the structure and optimization methods of machine learning models. The course is suitable for beginners in materials science, chemistry-related fields, and those interested in molecular simulation and machine learning. - Solid Electronic Structure
This course provides an overview of the history of solid-state physics, focusing on crystal structure and band theory. By introducing the periodic arrangement of crystals and their important influence on physical properties, it leads to Bloch's theorem, which reveals the behavior of electrons in one-dimensional and three-dimensional periodic fields. The course will also discuss the application of tight-binding approximation and its simplified analysis of band structures, and delve into the physical meaning of density of states and Fermi surfaces and their key roles in conductivity and semiconductor properties. This course is suitable for learners who wish to master the fundamentals of solid-state physics and understand its modern applications. The follow-up course is density functional theory. - Statistical Thermodynamics
This course mainly introduces the basic concepts of statistical thermodynamics commonly used in computational chemistry, as well as free energy calculation methods related to electrochemical calculations. - Free Energy Calculation Methods
This course introduces the concept of free energy from a statistical perspective, and connects it to similar concepts such as potential energy surfaces, analyzing the differences and connections between them. It introduces methods for calculating reaction free energy in computational chemistry, focusing on the principles and ideas of enhanced sampling methods that are currently widely used, as well as commonly used techniques in the research group. It ties back to the basic principles of free energy covered earlier. Finally, it demonstrates a simple free energy calculation example.