Designing Descriptors and Experimental Methods for High-throughput Electrolyte Discovery

Enabling energy-dense and cheap energy storage devices is critical to achieving decarbonization and tackling climate change. Developing advanced batteries requires electrolytes with optimal properties, but traditional trial-and-error methods are inefficient for exploring the vast space of electrolytes composed of various solvents, salts, and their combinations. To address this, the researchers propose to use machine learning (ML) and high throughput (HT) experimentation to accelerate electrolyte discovery. Their approach involves defining descriptors that accurately represent the desired electrolyte properties and ensuring compatibility with HT measurement systems. The proposed strategy addresses the issue of insufficient data for the training of ML models and unveils a new class of solvation-related descriptors that are relevant for battery chemistries. The interdisciplinary integration of ML, HT system, and electrochemistry promises to revolutionize the process of electrolyte discovery for next-generation batteries.

“By designing descriptors and experimental methods for high-throughput electrolyte discovery, this project seeks to fast-track the development of advanced, affordable energy storage solutions essential for decarbonization”

Chibueze Amanchukwu, Pritzker School of Molecular Engineering