Adapted from this Berkeley Lab press release
A team of Foundry staff and users have successfully demonstrated a machine-learning technique to accelerate discovery of materials for film capacitors — crucial components in electrification and renewable energy technologies. The technique was used to screen a library of nearly 50,000 chemical structures to identify a compound with record-breaking performance. Their results were reported in the journal Nature Energy.
“For cost-effective, reliable renewable energy technologies, we need better performing capacitor materials than what are available today,” said Yi Liu, a senior scientist at Berkeley Lab who led the study. “This breakthrough screening technique will help us find these ‘needle-in-a-haystack’ materials.”
There is rapidly growing demand for film capacitors for use in high-temperature, high-power applications such as electric vehicles, electric aviation, power electronics, and aerospace. Film capacitors are also essential components in the inverters that convert solar and wind generation into the alternating-current power that can be used by the electric grid.
Batteries receive a lot of attention as a workhorse in renewable energy applications, but electrostatic film capacitors are also important. These devices consist of an insulating material sandwiched between two conductive metal sheets. While batteries use chemical reactions to store and release energy over long periods, capacitors use applied electric fields to charge and discharge energy much more quickly.
Film capacitors are used for regulating power quality in diverse types of power systems. For example, they can prevent ripple currents and smooth voltage fluctuations, ensuring stable, safe, reliable operations.
Polymers — large molecules with repeating chemical units — are well-suited for the insulating material in film capacitors because of their light weight, flexibility, and endurance under applied electric fields. However, polymers have a limited ability to tolerate the high temperatures in many power system applications. Intense heat can reduce the polymers’ insulating properties and cause them to degrade.
Researchers have traditionally looked for high-performance polymers through trial and error, synthesizing a few candidates at a time and then characterizing their properties.
“Because of the pressing need for better capacitors, this approach is too slow to find promising molecules from the hundreds of thousands of possibilities,” said He Li, a postdoctoral researcher at Berkeley Lab.
To accelerate discovery, the research team developed and trained a set of machine-learning models known as feedforward neural networks to screen a library of nearly 50,000 polymers for an optimal combination of properties, including the ability to withstand high temperatures and strong electric fields, high energy storage density, and ease of synthesis. The models identified three particularly promising polymers.
Researchers from Scripps Research Institute synthesized the three polymers using a powerful technique, known as click chemistry, that rapidly and efficiently links together molecular building blocks into high-quality products. Scripps Professor Barry Sharpless, one of the lead researchers on the project, won a 2022 Nobel Prize in Chemistry for his research on the click-chemistry concept.
At the Foundry, the researchers fabricated film capacitors from these polymers and then evaluated both the polymers and capacitors. The team found that they had exceptional electrical and thermal performance. Capacitors made from one of the polymers exhibited a record-high combination of heat resistance, insulating properties, energy density, and efficiency. (A high-efficiency capacitor wastes very little energy when it charges and discharges.) Additional tests on these capacitors revealed their superior material quality, operational stability, and durability.
Read the full press release
