Researchers at Johns Hopkins Whiting School of Engineering used computer simulations to study how the arrangement of nanoscale metallic discs affects the optical behavior of plasmonic materials–metals designed to interact with light at extremely small scales. They found that a specific spatial pattern–known as a log-Gaussian Cox process–produced the broadest and most tunable range of optical behavior in advanced materials.
The research, led by Ekin Gunes Ozaktas, Engr ’24, now a PhD student at Stanford University, was published in Optics Express and could help improve solar panels, photodetectors, and other optoelectronic devices that rely on precise control of light.
“This kind of insight is crucial because it shows how mathematical modeling can play a central role in advancing material design,” said team member Eliza O’Reilly, an assistant professor of applied mathematics and statistics and a member of Johns Hopkins Data Science and AI Institute. “By harnessing controlled randomness, we’re not just analyzing spatial patterns—we’re actively shaping how materials interact with light. That matters because subtle differences in structure can significantly influence how effectively a material performs in technologies like solar energy or optical sensing.”
The researchers used spatial patterns that affect how the material absorbs or scatters light of different wavelengths. They then tested three types of spatial processes that produced different disc placements, and found that the clustered arrangements generated by the log-Gaussian Cox model offered the greatest flexibility and most tunable range of optical response. This showed that even subtle changes in how the discs are arranged at the nanoscale can significantly influence optical performance.
“We came up with a new way to measure how evenly materials respond to different colors of light,” said study co-author Susanna M. Thon, associate professor of electrical and computer engineering, associate director of the Ralph O’Connor Sustainable Energy Institute (ROSEI), and a member of the Data Science and AI Institute. “Some materials react very differently to light that’s just slightly different in color, and on a graph, that shows up as sharp peaks and valleys–what looks like a ‘spiky’ response. Other materials respond more evenly across the spectrum, which gives a much smoother response. That smooth response is what you want for things like solar panels, because it means that material can absorb energy from a wider range of sunlight colors.”
While the other two models–the Bernoulli process, which introduces randomness by removing discs from a grid, and the Strauss process, which simulates repulsion between discs to produce more evenly spaced layouts—are commonly used to study spatial randomness and ordering, they both allow limited tunability in optical responses. In contrast, the log-Gaussian Cox configurations provided two orders of magnitude of tunability in smoothness, making them especially promising for designing materials that can absorb a wide range of light.
“If you can model the structure with a tunable parameter that correlates with spectral properties, you can search more effectively for optimal configurations,” said O’Reilly.
O’Reilly, who specializes in point process models, says the study opens new possibilities for how disorder can be used in material design.
“Most prior work in the area has focused on periodic, evenly spaced layouts,” she said. “But this study suggests that disordered structures, if carefully designed, may unlock new optical behaviors.” She notes that this research also highlights a shift in how generative mathematical models can actively guide material design with targeted properties.
Although more work is needed to fully map the relationship between structure and optical performance, the team says its findings mark an important step toward using random geometry to design next-generation materials for electronics and energy harvesting.
“This is the closest my research on point processes has come to a tangible application like device design,” O’Reilly said. “It’s exciting to see these mathematical tools applied in such a concrete and impactful way.”
The study team also includes Sreyas Chintapalli, Engr’25 (PhD), now at NIST.