Fundamentals
Essential concepts and foundations for computational materials science
Introduction to ASE and DFT
Master the Atomic Simulation Environment (ASE) and learn how to perform DFT calculations with various quantum chemistry codes.
Python for Materials Science
Complete guide to setting up a Python environment for computational materials science with essential libraries and tools.
Crystal Structures & Symmetry
Understanding crystal structures, space groups, and symmetry operations for materials modeling and analysis.
DFT & Quantum Calculations
Advanced density functional theory methods and quantum mechanical simulations
Advanced VASP Calculations
Deep dive into VASP for complex materials calculations including surfaces, defects, and magnetic systems.
Phonons & Vibrational Properties
Calculate phonon spectra, thermal properties, and vibrational analysis for materials characterization.
Electronic Structure Analysis
Master band structure calculations, density of states, and electronic property analysis techniques.
Machine Learning for Materials
AI-driven approaches for materials discovery, property prediction, and automated workflows
ML for Materials Discovery
Introduction to machine learning techniques for accelerated materials discovery and property prediction.
Neural Network Potentials
Build and train machine learning potentials for large-scale molecular dynamics simulations with DFT accuracy.
Materials Descriptors & Features
Learn to generate and select meaningful descriptors for machine learning models in materials science.
Workflow Automation
High-throughput calculations, workflow management, and automated analysis pipelines
High-Throughput DFT Workflows
Design and implement automated workflows for large-scale materials screening and property calculations.
Materials Databases & Analysis
Build materials databases, perform data mining, and extract insights from computational results.
Cloud & HPC Computing
Deploy computational workflows on cloud platforms and high-performance computing clusters efficiently.
Recommended Learning Path
1. Fundamentals
Python & ASE Basics
2. DFT Methods
Quantum Calculations
3. Analysis
Data & Visualization
4. Machine Learning
AI for Materials
5. Automation
Workflows & Scale