Authors: Rasha Atwi | Nav Nidhi Rajput
Source: ECS Meeting Abstracts (article, 2022-07-07)
Field: Materials Science — Materials Chemistry

Tailoring Atomistic Interactions in Li-S Battery Via a Computational Multi-Scale-Data-Driven Approach A major breakthrough in battery materials is required to meet the ever-increasing proliferation of portable electronic devices, electric vehicles and their variants, as well as the need for incorporating renewable energy resources into the main energy supply [1]. In this context, lithium-sulfur (Li-S) batteries attract attention owing to their very high energy density (2,600 Wh kg -1 ) and specific capacity (1,675 mAh g -1 ) and significantly lower weight and cost, compared to lithium-ion batteries (LIBs) [2]. Fully packaged, it is expected that future Li-S batteries can operate at close to 500 Wh kg -1 , which is more than twice the energy density of LIBs (200 Wh kg -1 ) [3]. The problem of realizing the expected high energy density is defined by several issues including the dissolution of Li-Polysulfide (PS) species into the electrolyte, insulating properties of sulfur and Li-PS species, and volume change at the cathode [4]. Overcoming these challenges requires a fundamental understanding of the interplay between events occurring over wide spatial and temporal scales, and accurate prediction of electrode and electrolyte properties to obtain design metrics for new improved materials. In this talk, I will first discuss the details of a high-throughput multi-scale computational infrastructure developed by our group called MISPR (Materials Informatics for Structure-Property-Relationship) to design optimal materials for different applications such as Li-S batteries. MISPR is a high-fidelity and robust computational tool that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT) calculations through force field generation and information flow between the two-length scales. It allows automatic handling of thousands of computational materials science simulations and multiple systems with a strong focus on data provenance. I will then discuss the usage of MISPR to mitigate dissolution of LiPS species by altering the atomistic interactions between the electrolyte components through high-throughput screening of potential co-solvent molecules. This approach guides and accelerates our rational selection of co-solvents that enable optimal compromise between the solubility of PS species and the transport properties of the electrolyte through automated DFT calculations. We use the selected candidates in detailed MD studies to comprehend the relationship between the structure of the co-solvent and the electrolyte properties. The approach allows for creating a database of well-characterized materials to be used in machine learning-based methods as well as for testing computationally identified structures in experiments. This work provides crucial information to alleviate the dissolution of PS species during cycling, which is the main reason for rapid capacity decay in Li-S batteries. References Larcher, D. and J.-M. Tarascon, Towards greener and more sustainable batteries for electrical energy storage. Nature chemistry, 2015. 7 (1): p. 19. Manthiram, A., X. Yu, and S. Wang, Lithium battery chemistries enabled by solid-state electrolytes. Nature Reviews Materials, 2017. 2 (4): p. 1-16. Fang, R., et al., More reliable lithium‐sulfur batteries: status, solutions and prospects. Advanced materials, 2017. 29 (48): p. 1606823. Manthiram, A., Y. Fu, and Y.-S. Su, Challenges and prospects of lithium–sulfur batteries. Accounts of chemical research, 2013. 46 (5): p. 1125-1134. Figure 1 Polysulfide ; Battery (electricity) ; Context (archaeology) ; Density functional theory ; Computer science ; Electrolyte ; Energy storage ; Cathode ; Lithium (medication) ; Lithium–sulfur battery ; Scale (ratio) ; Nanotechnology ; Materials science ; Engineering physics ; Chemistry ; Electrode ; Electrical engineering ; Engineering ; Physics ; Power (physics) ; Computational chemistry ; Thermodynamics Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Fuel Cells and Related Materials