91�ؿ�

If you walk into an 91�ؿ� Tech physics lab, you might hear references to something called Neo.

No, the talk isn’t about the high-flying hero of the sci-fi trilogy The Matrix. It’s about an innovative pair of data-analysis tools—X-ray photoelectron spectroscopy (XPS) Neo and X-ray emission spectroscopy (XES) Neo—designed by 91�ؿ� Tech Ph.D. student Alaina Humiston (Ph.D. PHYS 6th Year) that have the potential to transform how scientists analyze complex materials data.

“The program name actually comes from the character that you know in The Matrix,” says Alaina. “When you run the code, it looks like The Matrix running across your screen. That’s how it got the name.”

The software builds on a long-running project that includes extended X-ray absorption fine structure Neo and nanoindentation (Nano-Neo), but Alaina’s work is helping it evolve into something far more powerful—and, critically, accessible—to scientists around the world. The core problem that their work addresses is a significant problem in X-ray photoelectron spectroscopy: bad data fits. These bad data fits are incorrect or poorly constructed models of XPS information that result from choosing the wrong parameters, peak shapes, backgrounds, or constraints, as well as noisy or cropped data. These errors often result in unreliable or misleading conclusions.

“This data has been shown in the literature to have a large amount of inexperienced users publishing really bad data fits,” says Alaina. “There is a big need for a software that has more knowledge about the data you’re fitting.”

That’s where Neo comes in. By embedding expert knowledge directly into its algorithm, the software helps steer users away from common mistakes. The result is an analysis that’s more accurate, more reliable, and more scientifically meaningful.

The idea is simple: build a tool that catches common mistakes before they happen.

“Having an algorithm that already has this information built into it can help steer these fits away from some really big mistakes,” adds Alaina.

This project hasn’t gone unnoticed. Alaina’s work on Neo has garnered recognition, winning a poster award at the ICESS-16 conference this summer and has been featured on the Journal of Electron Spectroscopy and Related Phenomena website.

Alaina and their research group have already publicly released XES Neo on GitHub—a platform where developers can share their code, among other things—with XPS Neo soon to follow.

The next steps for Neo are even more ambitious.

With funding from Los Alamos National Laboratory, Alaina is helping develop the algorithm further so it can handle extremely challenging datasets—some of which involve notoriously complex materials such as plutonium. Part of that effort includes building an online library of high-quality reference data that Neo can learn from.

“We want this algorithm to be able to fit very difficult data,” says Alaina. “We’re striving for an algorithm that learns based on information it has.”

More than anything, Alaina wants this work to remain open to all—accessible, transparent, and freely usable. That’s why they’re making it publicly available to anyone who needs it.

“It’s a technique that a lot of people are using; it’s become a lot more popular over just the past couple of decades,” says Alaina. “Having an algorithm like this out there for others to use—it’s not behind any paywall—is something that I really enjoy. I believe that information should be available for everyone.”

As Neo continues to evolve, so does the impact of Alaina’s work. By pairing technical expertise with a commitment to open access, Alaina is helping to open the door to better science, broader collaboration, and discoveries that reach far beyond the walls of any single lab.