Accelerating Battery Electrolyte Design Process Via Closed-Loop Optimization of Simulations and Experiments

Abstract

Designing and discovering new material combinations for battery applications has been and remains to be a slow and expensive process. It is a challenging task to develop battery electrolytes that are safe, high-performant, and cost-effective, as there are many design parameters to consider. There are many recent efforts to expedite such a process, ranging from the use of machine learning optimization techniques and lab automation to utilizing computer simulations.We propose an automated, closed-loop optimization strategy to design electrolytes by integrating machine learning-based optimization algorithms, autonomous robotics, and molecular simulations. In particular, we demonstrate the application of our approach for designing room-temperature ionic liquids (RTILs) for Li-ion batteries, which consist of multiple salts, cations, and anions. The composition space is too vast to explore via a conventional approach. Thus, we use intelligent decision-making algorithms for selecting which composition to test next, which is carried out by the in-house robotic platform. The molecular simulations are also used to provide an in-depth understanding of the experimentally observed behavior, and the simulation results are also fed into the optimization algorithm. The presented approach is designed to be general, so it can be applied to other electrochemical applications with minimal modifications to the algorithms or the experimental setup.