Computational Chemistry Software
Introduction
This series of courses covers practical training on mainstream computational tools such as VASP, Quantum Espresso (QE), and CP2K, along with modeling basics using ASE and Packmol, and the efficient application of ai²-kit. The courses systematically guide participants through software usage, optimization, and molecular dynamics simulations.
Course List
- Basics of ASE & Packmol Modeling
This course introduces two modeling tools, ASE and Packmol. Topics include atomic manipulation, visualization, interface and bulk structure modeling with ASE, as well as Packmol input formats and keywords (e.g., inside cube, outside cube). - Hands-on Training with Quantum EspressO / VASP
This course provides practical instruction on VASP and QE, covering structure optimization, static self-consistent calculations, and band structure calculations. It explains input/output file preparation, parameter settings, job submission and monitoring, and result analysis with visualization tools, supported by detailed case studies. - Molecular Dynamics / Machine Learning Molecular Dynamics Practice
This course introduces the fundamental principles of Classical Molecular Dynamics (CMD) and Machine Learning Molecular Dynamics (MLMD). It covers how MD simulations are applied to study physicochemical properties of molecules and materials, and how machine learning methods can be used to construct potential energy functions to enhance efficiency and accuracy. The CMD part introduces the principles of MD, interatomic potentials and classical force fields, numerical solutions to Newton’s equations of motion, statistical ensembles, methods of temperature and pressure control, and practical implementation of MD simulations. The MLMD part explains the relationships between machine-learning-based potentials, classical mechanics, and quantum mechanics; principles of MLMD; concepts of atomic energy and deep potential models of potential energy surfaces; machine learning model architectures and optimization techniques. - CP2K Practice
This course focuses on the efficient application of the CP2K software for computational simulations. The content is divided into five sections, designed to guide newcomers from the fundamentals to more advanced computational skills:
①Single-point energy calculation: Single-point energy represents the total energy of a molecule at a specific geometry and serves as the foundation for understanding molecular properties.
②Static structure optimization: By adjusting the molecular geometry to reach the lowest-energy stable configuration, one can reveal the true equilibrium structure of the molecule.
③NEB transition state search: Construction of NEB pathways and analysis of transition-state geometries and energy profiles provide deeper insights into the mechanisms of chemical reactions.
④Frequency calculation: This step reveals molecular vibrational modes, including normal modes and imaginary frequencies, thereby clarifying molecular stability and reactivity.
⑤Molecular dynamics simulation Simulating molecular motions and interactions over extended time scales offers valuable understanding of dynamic behaviors. - Introduction to ai²-kit
Efficient training of machine-learning interatomic potentials relies on effective dataset generation. Through the synchronized learning workflow of ai²-kit, users can efficiently explore unknown chemical space with molecular dynamics, thereby expanding datasets. This approach can also be coupled with enhanced sampling techniques to further improve sampling efficiency.