Accelerated Electrolyte Discovery using Data Driven Approach

Abstract

Lithium-ion batteries (LIBs) have advanced significantly as an essential energy storage solution for the last decade. However, gradual saturation in achievable energy density, flammability, high cost, and environmental impacts of critical raw materials (ex. cobalt and nickel) have yet to be addressed. This enhances the need to discover and develop new next-generation battery technologies that utilize sustainable materials and encompasses improved performance for a range of applications including electric vehicles and stationary energy storage. This work focusses on discovery of safe and high-performance liquid electrolytes for next generation battery systems by using data driven methodologies such as QSAR (Quantitative Structure to Activity Relationship) prediction models and formulants-to-performance mapping deep learning models. Generally, electrolyte constituents are screened-based on one or more essential properties such as their stability in electrochemical window, solubilities of salts in solvents, and ionic conductivities. Advancements in computation techniques allow easy assessment of these properties for selection of suitable electrolyte constituents through high throughput screening process called computational funneling. In our work, we further expand the electrolyte discovery workflow to the next stage of finding the right composition of electrolyte constituents (solvents, co-solvents, salts) using deep learning, that may otherwise involve high throughput experimentation in a relatively larger chemical space. It has been reported that exhaustively searching 4 component mixtures of electrolytes would require over a million evaluations. To optimally search the formulation design space for target performance, we adopt a simulation-experiment-AI synergistic approach where the initial battery cell tests that are used to develop battery chemistry are used for model learning and then driving the optimization of new electrolyte formulation, resulting in a fewer number of lab- experiments for validation. In this talk we will discuss our data-driven electrolyte discovery workflow and toolkit, along with a use case demonstration conducted with a next-generation lithium-metal battery based on iodine-conversion chemistries.