If you’ve ever gone out on a sunny day and slathered on some sunblock, you might have been employing millions of tiny metallic tools to help deflect the UV light that threatens to burn your skin. Metal nanoparticles are used in a large variety of products, from titanium oxide in some sunscreen lotions to silver nanoparticles that are used in certain medical bandages due to their natural antibacterial nature. New applications for these powerful tools are being discovered in labs each year, and that’s just with the nanoparticles we know of. 

Researchers at Penn State were interested in finding new metal nanoparticles, but to do so would require huge computational resources to run the required simulations. Huaizhong Zhang, a graduate student, and Kristen Fichthorn, a professor of physics, used an ACCESS allocation to get the power they needed to conduct their research. They chose Pittsburgh Supercomputing Center’s (PSC) Bridges-2 supercomputer to examine copper and silver nanoparticle structures by using Artificial Intelligence (AI) to create detailed simulations of the tiny particles.

[Bridges-2] is essential. We wouldn’t use Bridges if it wasn’t essential, because we have our own [computing] facilities at Penn State. Just to be able to pull these off, to be able to fit these force fields, takes Bridges’ extreme memory.

–Kristen Fichthorn, Penn State

With the computational power of Bridges-2’s four extreme memory nodes, the Penn State team was able to find 20 types of nanoparticles that had never been found using traditional research methods. The team published their results in the journal Nanoscale and the work was so promising they’re planning to continue the study and publish future research related to this project.

You can find the in-depth story on this research here: Penn State Team Uses AI to Predict Dozens of Undiscovered Metal Nanoparticles

Resource Provider Institution(s): Pittsburgh Supercomputing Center (PSC)
Affiliations: Penn State
Funding Agency: NSF
Grant or Allocation Number(s): DMR110061

The science story featured here was enabled by the U.S. National Science Foundation’s ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.