Accelerating Sustainable Electrolyte Design: A Self-Driving Approach to High-Entropy Formulations
Jin Hyun Chang a
a Technical University of Denmark (DTU), Denmark
Proceedings of MATSUS Spring 2026 Conference (MATSUSSpring26)
I4 Digital Discovery: From Energy Materials to Devices
Barcelona, Spain, 2026 March 23rd - 27th
Organizers: Shoichi Matsuda and Magda Titirici
Invited Speaker, Jin Hyun Chang, presentation 764
Publication date: 15th December 2025

The transition to sustainable energy hinges on next-generation batteries with enhanced performance, reduced toxicity, and lower manufacturing costs. Current state-of-the-art liquid electrolytes rely on hazardous and difficult-to-recycle components and demand energy-intensive, ultra-dry manufacturing environments. High-entropy electrolytes (HEEs), which utilize a mixture of multiple salts and solvents to leverage synergistic entropic effects, offer a promising pathway to simultaneously improve ionic conductivity, broaden the electrochemical stability window, and reduce component cost. However, the vast, non-intuitive compositional space of HEEs makes their discovery prohibitively slow using traditional trial-and-error methods.

This presentation details a new paradigm in electrolyte research, implementing a fully self-driving laboratory (SDL) pipeline to accelerate the discovery and optimization of HEEs for sustainable batteries. The integrated closed-loop system combines:

1. Atomistic insights from molecular dynamics simulations to screen candidate component combinations based on transport (e.g., diffusivity, ionic conductivity) and solvation structures (e.g., ion clustering), which are essential descriptors for HEE performance. 

2. Autonomous experimentation to perform a high-throughput formulation and characterization to assess ionic conductivity and electrochemical stability window.

3. Active Learning (Bayesian Optimization) to decide the next experimental point by strategically balancing the exploitation of known good regions of the HEE design space with the exploration of chemically uncertain, high-potential regions. 

By integrating simulations and closed-loop experimentation, our methodology dramatically reduces the discovery cycle. This data-driven approach establishes a new paradigm for materials science and provides a validated, accelerated route toward identifying novel HEEs that are anticipated to meet commercial performance metrics and pave the way for scalable and environmentally benign battery technology.

This research is supported by a grant from the Villum Experiment program, VILLUM FONDEN.

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