Ab Initio Prediction of Metal Phosphide Anode Materials for Lithium and Beyond Lithium Batteries
type: CreativeWork
description: Identifying high capacity battery materials is critical for creating better energy storage to lower our reliance on non-renewable energy resources. While Li-ion batteries are the state-of-the-art, their graphite anodes are limited by a theoretical capacity of 372 mAh/g. Phosphorus is one alternative which has a high capacity of 2596 mAh/g and can alloy with both Li$^+$ and Na$^+$ ions, but suffers from large volume changes upon cycling. To mitigate this destructive effect, transition metals act as stabilising agents, limiting volume change and retaining high capacities. In this dissertation, I investigate two classes of transition metal phosphides (TMPs) as candidates for high capacity Li and Na-ion battery anodes. Herein, I employ a computational approach which combines density-functional theory (DFT) with structure searching methods including $Ab$ $Initio$ Random Structure Searching (AIRSS) and Genetic Algorithms (GA). I conduct an AIRSS and GA search of the Li-Cu-P system, as well as an AIRSS search of the Na-Fe-P system, and study their ground state electrochemical properties with DFT. I investigate the lithiation pathway in Cu-P, and find that LiCu may form during cycling, increasing the overall capacity of all Cu-P anodes. Additionally, I calculate the capacity of CuP$_{10}$, to be 2225 mAh/g, while the highest capacity Cu-P to date is CuP$_2$ at 1495 mAh/g. This suggests that it should be tested in future experimental work. Using AIRSS, I identify a ground state $I$mm2 Cu$_2$P structure, which has not been identified experimentally, and find it is a stable semimetal at high temperature and pressures up to 10 GPa. I also find an AIRSS identified structure of Cu$_3$P with Cu vacancies (Cu$_8$P$_3$) which has different vacancy orderings to previously identified Cu$_{3-x}$P, suggesting this structure has several possible ground state orderings. Finally, I assess the effects of pressure on Cu-P, and find that several GA-identified $P$1 structures are low in energy…
abstract: Identifying high capacity battery materials is critical for creating better energy storage to lower our reliance on non-renewable energy resources. While Li-ion batteries are the state-of-the-art, their graphite anodes are limited by a theoretical capacity of 372 mAh/g. Phosphorus is one alternative which has a high capacity of 2596 mAh/g and can alloy with both Li$^+$ and Na$^+$ ions, but suffers from large volume changes upon cycling. To mitigate this destructive effect, transition metals act as stabilising agents, limiting volume change and retaining high capacities. In this dissertation, I investigate two classes of transition metal phosphides (TMPs) as candidates for high capacity Li and Na-ion battery anodes. Herein, I employ a computational approach which combines density-functional theory (DFT) with structure searching methods including $Ab$ $Initio$ Random Structure Searching (AIRSS) and Genetic Algorithms (GA). I conduct an AIRSS and GA search of the Li-Cu-P system, as well as an AIRSS search of the Na-Fe-P system, and study their ground state electrochemical properties with DFT. I investigate the lithiation pathway in Cu-P, and find that LiCu may form during cycling, increasing the overall capacity of all Cu-P anodes. Additionally, I calculate the capacity of CuP$_{10}$, to be 2225 mAh/g, while the highest capacity Cu-P to date is CuP$_2$ at 1495 mAh/g. This suggests that it should be tested in future experimental work. Using AIRSS, I identify a ground state $I$mm2 Cu$_2$P structure, which has not been identified experimentally, and find it is a stable semimetal at high temperature and pressures up to 10 GPa. I also find an AIRSS identified structure of Cu$_3$P with Cu vacancies (Cu$_8$P$_3$) which has different vacancy orderings to previously identified Cu$_{3-x}$P, suggesting this structure has several possible ground state orderings. Finally, I assess the effects of pressure on Cu-P, and find that several GA-identified $P$1 structures are low in energy…
author: Harper, Angela F
language: en
keywords: lithium ion batteries, condensed matter physics, computational physics, physics, materials science, density functional theory, crystal structure prediction
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/9999fcac-29d9-42de-9c01-5a98c21ce9ff/download
license: https://www.rioxx.net/licenses/all-rights-reserved/
date published: 2019-02-23
date modified: 2026-07-22T22:24:13Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.30542