Historically, researchers in materials discovery have devised and tested options through some mix of hunches, informed speculation, and trial by error. But it’s a difficult and time-consuming process simply given the vast array of possible substances and combinations, which can send researchers down numerous false paths.
In the case of electrolyte ingredients, “you can mix and match them in billions of ways,” says Venkat Viswanathan, an associate professor at Carnegie Mellon, a co-author of the Nature Communications paper, and a cofounder and chief scientist at Aionics. He collaborated with Jay Whitacre, director of the university’s Wilton E. Scott Institute for Energy Innovation and the co-principal investigator on the project, along with other Carnegie researchers to explore how robotics and machine learning could help.
The promise of a system like Clio and Dragonfly is that it can rapidly work through a wider array of possibilities than human researchers can, and apply what it learns in a systematic way.
Dragonfly isn’t equipped with information about chemistry or batteries, so it doesn’t bring much bias to its suggestions beyond the fact that the researchers select the first mixture, Viswanathan says. From there, it runs through a wide variety of combinations, from mild refinements of the original to completely out-of-the-box suggestions, homing in on a mix of ingredients that delivers better and better results against its programmed goal.
In the case of battery experiments, the Carnegie Mellon team was looking for an electrolyte that would speed up the recharging time for batteries. The electrolyte solution helps shuttle ions—or atoms with a net charge due to the loss or gain of an electron—between the two electrodes in a battery. During discharge, lithium ions are created at the negative electrode, known as the anode, and flow through the solution toward the positive electrode, the cathode, where they gain electrons. During charging, that process is reversed.