Phase Stability and Intercalant Ordering in V2O5 Cathodes

Summary

We will use cluster expansions, constructed based on density functional theory (DFT) calculations, to investigate the phase diagram upon intercalation of V2O5, a potential cathode material for non-Li-ion batteries, i.e., batteries that use ions such as Na, Mg, Ca, etc. Cluster expansions are required to be able to run large-scale Monte Carlo simulations. These will be used to introduce temperature to the DFT results, so that phase diagrams for different polymorphs and intercalant concentrations can be obtained. This will also allow for voltage profiles to be calculated.

Job Description

The student will create python scripts using the Atomic Simulation Environment (ASE) framework and the CLuster Expansion in Atomic Simulation Environment (CLEASE) python package to construct cluster expansions for different polymorphs of V2O5 and for different intercalated ions. These expansions will be fitted using machine-learning approaches, and will be based on density functional theory (DFT) calculations, as implemented in the VASP package. These calculations will be parallelized using MPI (both the VASP calculations, but also the Monte Carlo simulations). Results will be stored in databases. The student will learn to interact with high-performance computing resources, and will write code to perform all steps: underlying DFT calculations, fitting of cluster expansions using machine-learning approaches, such as compressed sensing, creating Monte Carlo simulations based on the cluster expansions, and scripts to plot and analyze results.

Computational Resources

The DFT calculations will be performed on XSEDE (in particular on PSC Bridges-2), and will be controlled by scripts written in python (via ASE). Such calculations will typically require 1 node (128 cores on Bridges-2) for 4-6 hours. Fitting of the cluster expansions will be done on the studentâ€™s computer and/or the local cluster, and subsequent Monte Carlo simulations will be done on XSEDE machines.

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 studentâ€™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 better understanding of, and potential improved, battery cathodes for novel non-Li-ion batteries.

Position Type

Apprentice

Training Plan

The student will learn how to code in python, will learn solid state physics, thermodynamics, and quantum mechanics (as needed), 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 (through Zoom), combined with online tutorials. Initial testing will be done on a known (and quick to calculate) model system (Au-Cu alloys), so that the student can gain confidence in the methodology before applying it to a completely unknown system (intercalated V2O5). 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, analyze 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

Basic proficiency with linux, python, hpc environments.