The purpose of this page is to summarize the basic practices and key guidelines that all group members should follow. This includes everything from the initial steps you should take upon joining to the foundational knowledge you need to succeed in our work. Understanding and adhering to these practices will make you better prepared to collaborate effectively and contribute meaningfully to our research.
On this webpage, we summarize the main guidelines and suggestions for newcomers and people closely working with us on obtaining supercomputer resources, collaboration strategies, etc.
Writing environment: GoogleDocs, overleaf (Please note that online platform usage reduces the number of emails, clarity of who did what, summarize progress, etc.). Any project should start with a proper literature review and a quick summary of the research opportunity.
Introduction to Main Computational Methods and Python:
Simulation Techniques: Familiarize yourself with the primary computational methods used in our research, such as density functional theory (DFT), molecular dynamics (MD), and Monte Carlo simulations.
Software and Tools: Learn about the key software packages and computational tools that are standard in our group, including VASP, LAMMPS, Quantum ESPRESSO, and others.
Training Materials: Access tutorials that will help you quickly learn these computational methods and tools.
Access to Supercomputers and Data Storage Policy:
HPC Resources: Get detailed information on accessing high-performance computing (HPC) resources available to our group.
Account Setup and Configuration: Step-by-step instructions on setting up your HPC accounts, configuring your environment, and ensuring your computational work is optimized.
Usage Guidelines: Best practices for efficient and responsible use of HPC resources, including job submission protocols and troubleshooting tips.
Main software and libraries:
Writing environment: GoogleDocs, overleaf (Please note that online platform usage reduces the number of emails, clarity of who did what, summarize progress, etc.). Any project should start with a proper literature review and a quick summary of the research opportunity.
Visualization - Vesta (Please read the official website and manual).
Main Python libraries for the data analysis of first-principles calculations (pymatgen, Aiida, ASE, and pylada). Please note that usage of codes requires a full understanding of what they do.
Phonon calculations - phonopy
Main Python libraries for defect calculations: doped and pylada-defects. Please note that using codes requires a complete understanding of defect theory.
Python installations (Anaconda - winner among my postdocs/students; Notepad++/Sublime text - still preferred by me despite many years)