NCSI: A First-principles Investigation of V2O5 as a Sensor

Status	Completed
Mentor Name	Hartwin Peelaers
Mentor's XSEDE Affiliation	Research Allocation
Mentor Has Been in XSEDE Community	4-5 years
Project Title	A First-principles Investigation of V2O5 as a Sensor
Summary	Using density functional theory, as implemented in the VASP code, and python scripts based on the Atomic Simulation Environment (ASE), we will investigate if V2O5, a layered material, can be used as a sensor material for simple molecules, such as NH3, CO, etc. Some experiments reveal that that should be possible, and in this project we will unravel the mechanisms at an atomic level. Initial student research focused on using the quantum mechanical calculations as a black box to obtain forces and energies, which were then interpreted classically. In this continuation, we will consider the underlying calculations in more detail, and their quantum mechanical nature (e.g., wavefunctions, densities, band structures, etc.).
Job Description	The student will use python scripts, using the Atomic Simulation Environment (ASE) framework, to perform simulations using density functional theory (DFT) of investigate the mechanisms of adsorption and desorption of small molecules (CO, NH3, H2O, etc.) on V2O5 surfaces. The framework initially hides much of the quantum mechanical nature and the details of the DFT simulations (using the VASP code). This allows the student to focus on the programming and analysis first. The student will learn python (object oriented code and parallelizing of python loops), and version control using git. Calculations to run will be saved to a database, and the obtained results will also be saved in a database. The student will learn to interact with this database. The actual DFT calculations will be performed on the XSEDE machines or on the local cluster. Each individual calculation will be parallelized. The student will learn how to interact with HPC computing facilities and their queuing system, and will write basic python code to do these interactions automatically. This will also require to write code to correct common occurring errors. In Spring, we will gradually introduce quantum mechanical information of the underlying first-principles code.
Computational Resources	All underlying DFT calculations will be performed on the XSEDE machines, with additional testing and debugging on the local cluster. A typical DFT job will require 1-4 nodes and 24-48 hours of running time.
Contribution to Community	This project will train an undergraduate student in the use of HPC resources to perform computational research on materials properties using first-principles codes. This will increase the student’s knowledge and proficiency in HPC computing and the underlying physics and material science, which are valuable skills. The project scientific goals might lead to new and improved sensor materials.
Position Type	Apprentice
Training Plan	The student will learn how to code in python, basic solid state physics (as needed), basic quantum mechanics (starting in Spring), basic linux shell (cd, mkdir, cp, ls, etc.), and interact with HPC resources (ssh, scp, slurm, etc.). This learning will occur through 1-on-1 mentoring with the PI, combined with online tutorials and specific tasks (e.g., write a simple python script to do x). Once initial results are obtained, the student will study these based on guiding questions. The more experienced the student becomes, the more open questions and tasks will become. This will introduce the student gradually to more aspects of the scientific process (making hypothesis, designing calculations to test these, analyse results, refine hypothesis, and so on). The student will also take part in regular group meetings to learn about other group members' research and discussions about that research.
Student Prerequisites/Conditions/Qualifications	This is a continuation of a project, so basic proficiency with python, shell, HPC computing, and the ASE framework is required.
Duration	Semester
Start Date	02/01/2021
End Date	04/26/2021