Using the Electronic Charge Density to Analyze Cathode Materials

We have developed a full suite of tools for analyzing the performance of any materials in cathode applications using the electronic charge density. The new framework required advances in algorithms and data storage strategies that push the boundaries of open-source and open-data materials sciences.
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As materials science evolves into a software and data-driven field, it is important to build more sophisticated software tools for materials analysis. Our recent works on identifying the metastable positions of Li/Mg ions in solid-state materials and on analyzing their migration in a graph theoretic framework were both enabled by the development of such software tools. Identification of meta-stable cation positions in arbitrary crystal structures is a non-trivial task, all methods based on atomic structure analysis are limited to specific structure motifs and are not generalizable. We found that by analyzing the electronic charge density computed from first principles, we can identify the most likely cation intercalation sites [1] and narrow down the list of possible migration pathways [2] through a given material. The metastable cation positions can be connected to form a periodic graph that represents the complete picture of the migration characteristics of Li/Mg ions in the material [1]. Then, the charge density can be used again to either approximate the migration energy barrier in a single particle picture or be used to accelerate more expensive migration energy barrier calculations [2]. In our recent publications, we have demonstrated that the charge density is a powerful tool in analyzing material properties and have developed the data and software infrastructure to allow users to access these tools and the data they generate. These tools represent a complete analysis pipeline to investigate the thermodynamic and kinetic properties of cathode materials from first principles, and they form the foundation of a toolkit for new cathode materials discovery and can be used to accelerate the development of new cathode materials for Li-ion and Mg-ion batteries.

The storage strategy and applications of volumetric data for cathode material analysis.

While performing this type of analysis for a single material is relatively straightforward, performing this analysis on thousands of materials is significantly more challenging. On top of the open-sourcing computational methodology, distributing the results of this analysis to the community as a searchable database is even more challenging. Specifically, working withvolumetric data (like the charge density) on a database scale was complicated by the large size of the data and the lack of a suitable database model. We were able to overcome these challenges by developing a hybrid data storage model that combines the query capabilities of MongoDB with the data storage capabilities of the AWS S3 [3]. This allowed users to bypass the typically small document size limit of MongoDB and work with arbitrarily large data sets. Using these new tools we were able to compute and analyze hundreds of thousands of electronic charge densities to identify new metastable cation positions which yielded many new data points for the Materials Project cathode materials database. In addition to the methodologies detailed in Ref. [1-3], we also encourage the reader to explore the cathode materials database on the Materials Project and see how their favorite cathode materials perform.

1. Shen, J.-X., Horton, M., & Persson, K. A. (2020). A charge-density-based general cation insertion algorithm for generating new Li-ion cathode materials. npj Comput Mater, 6(161), 1–7. doi: 10.1038/s41524-020-00422-3

2.Shen, J.-X., Li, H. H., Rutt, A., Horton, M. K., & Persson, K. A. (2023). Topological graph-based analysis of solid-state ion migration. npj Comput. Mater., 9(99), 1–5. doi: 10.1038/s41524-023-01051-2

3. Shen, J.-X., Munro, J. M., Horton, M. K., Huck, P., Dwaraknath, S., & Persson, K. A. (2022). A representation-independent electronic charge density database for crystalline materials. Sci Data, 9(661), 1–7. doi: 10.1038/s41597-022-01746-z

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