Discovery of High-Capacity Oxysulfide Cathode Materials with Reversible Anion Redox
Abstract
For decades, battery design and development was based on the idea that the only possible source of redox in Li-ion cathodes was on the transition metal. More recently, the idea that the anion serves as a possible redox center has been proposed and investigated.Anion redox would serve as an additional source of capacity and energy density for batteries, and hence would seem to be highly desirable. However, anion redox is often associated with irreversible oxygen release from battery cathodes, and this serves as a significant challenge to the use of anion redox to design improved batteries. What is required is a material that is capable of undergoing stable, reversible anion redox (along with cation redox) without significant oxygen loss, across many cycles that form the lifetime of the battery.Here, we propose an ambitious, innovative three-year research plan that will utilize large-scale, high-throughput computational tools, combined with data-driven machine learning approaches to 1) understand the chemical origins of anion redox, 2) determine design strategies to tune anion redox behavior, and 3) efficiently search for novel, multicomponent oxysulfide cathode materials featuring high reversible anion capacity. We will perform this work searching over a large number of possible oxysulfide materials. Sulfur is used as an alloying element (with oxygen) on the anion site because of its utility to tune the anion p-state energy levels, hypothesized to play a key role in the anion redox stability and reversibility. The first stage of our project will involve high-throughput exploration of a large range of cation chemistries in oxysulfide compounds, and the analysis of competition between cation and anion redox when these materials are oxidized. This high-throughput screening in the first stage will lead to discovery of novel, high-capacity materials, but will also provide a training set for data-driven and machine learning analysis. This application of machine learning methods to the large-scale data will enable the second stage: a large-scale data-driven analysis from the resulting computational database that will allow us to find a consistent, universal model for the reversible anion redox mechanismin oxysulfides, across a wide range of cation chemistries. This mechanistic understanding will, in turn enable the third stage: allowing for an acceleration in the subsequent discovery of even better cathode materials with high anion redox capacity. The successful completion of this project will result in: (i) an unsurpassed understanding of the role of chemistry and crystal structure in reversible anion redox, (ii) a series of computationally predicted material compositions that exhibit superior battery cathode properties, and (iii) a very large dataset of oxysulfide battery cathode properties that will not only aid in the completion of this project s goals, but will also provide useful information for many future studies as well.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 24, 2023
- Source ID
- N000142312311
Entities
People
- Christopher Wolverton
Organizations
- Northwestern University
- Office of Naval Research
- United States Navy