Quantum sensors detect the smallest of environmental changes — for example, an atom reacting to a magnetic field. As these sensors “read” the unique behaviors of subatomic particles, they also dramatically improve scientists’ ability to measure and detect changes in our wider environment.

Monitoring these tiny changes results in a wide range of applications — from improving navigation and natural disaster forecasting, to smarter medical imaging and detection of biomarkers of disease, gravitational wave detection, and even better quantum communication for secure data sharing.

Georgia Tech physicists are pioneering new quantum sensing platforms to aid in these efforts. The research team’s latest study, “Sensing Spin Wave Excitations by Spin Defects in Few-Layer Thick Hexagonal Boron Nitride” was published in Science Advances this week. 

The research team includes School of Physics Assistant Professors Chunhui (Rita) Du and Hailong Wang (corresponding authors) alongside fellow Georgia Tech researchers Jingcheng Zhou, Mengqi Huang, Faris Al-matouq, Jiu Chang, Dziga Djugba, and Professor Zhigang Jiang and their collaborators. 

An ultra-sensitive platform

The new research investigates quantum sensing by leveraging color centers — small defects within crystals (Du’s team uses diamonds and other 2D layered materials) that allow light to be absorbed and emitted, which also give the crystal unique electronic properties. 

By embedding these color centers into a material called hexagonal boron nitride (hBN), the team hoped to create an extremely sensitive quantum sensor — a new resource for developing next-generation, transformative sensing devices. 

For its part, hBN is particularly attractive for quantum sensing and computing because it could contain defects that can be manipulated with light — also known as "optically active spin qubits."

The quantum spin defects in hBN are also very magnetically sensitive, and allow scientists to “see” or “sense” in more detail than other conventional techniques. In addition, the sheet-like structure of hBN is compatible with ultra-sensitive tools like nanodevices, making it a particularly intriguing resource for investigation.

The team’s research has resulted in a critical breakthrough in sensing spin waves, Du says, explaining that “in this study, we were able to detect spin excitations that were simply unattainable in previous studies.” 

Detecting spin waves is a fundamental component of quantum sensing, because these phenomena can travel for long distances, making them an ideal candidate for energy-efficient information control, communication, and processing.

The future of quantum

“For the first time, we experimentally demonstrated two-dimensional van der Waals quantum sensing — using few-layer thick hBN in a real-world environment,” Du explains, underscoring the potential the material holds for precise quantum sensing. “Further research could make it possible to sense electromagnetic features at the atomic scale using color centers in thin layers of hBN.”

Du also emphasizes the collaborative nature of the research, highlighting the diverse skill sets and resources of researchers within Georgia Tech. 

“Within the School of Physics, Professor Zhigang Jiang's research group provided the team with high-quality hBN crystals. Jingcheng Zhou, who is a member of both Professor Hailong Wang’s and my research teams, performed the cutting-edge quantum sensing measurements,” she says. “Many incredible students also helped with this project.”

Du is a leading scientist in the field of quantum sensing — this year, she received a new grant from the U.S. Department of Energy, along with a Sloan Research Fellowship for her pioneering work on developing state-of-the-art quantum sensing techniques for quantum information technology applications. The prestigious Sloan award recognizes researchers whose “creativity, innovation, and research accomplishments make them stand out as the next-generation of leaders in the fields.” 

DOI: 10.1126/sciadv.adk8495

This work is supported by the U. S. National Science Foundation (NSF) under award No. DMR-2342569, the Air Force Office of Scientific Research under award No. FA9550-20-1-0319 and its Young Investigator Program under award No. FA9550-21-1-0125, the Office of Naval Research (ONR) under grant No. N00014-23-1-2146, NASA-REVEALS SSERVI (CAN No. NNA17BF68A), and NASA-CLEVER SSERVI (CAN No. 80NSSC23M0229).

Energy is everywhere, affecting everything, all the time. And it can be manipulated and converted into the kind of energy that we depend on as a civilization. But transforming this ambient energy (the result of gyrating atoms and molecules) into something we can plug into and use when we need it requires specific materials.

These energy materials — some natural, some manufactured, some a combination — facilitate the conversion or transmission of energy. They also play an essential role in how we store energy, how we reduce power consumption, and how we develop cleaner, efficient energy solutions.

“Advanced materials and clean energy technologies are tightly connected, and at Georgia Tech we’ve been making major investments in people and facilities in batteries, solar energy, and hydrogen, for several decades,” said Tim Lieuwen, the David S. Lewis Jr. Chair and professor of aerospace engineering, and executive director of Georgia Tech’s Strategic Energy Institute (SEI).

That research synergy is the underpinning of Georgia Tech Energy Materials Day (March 27), a gathering of people from academia, government, and industry, co-hosted by SEI, the Institute for Materials (IMat), and the Georgia Tech Advanced Battery Center. This event aims to build on the momentum created by Georgia Tech Battery Day, held in March 2023, which drew more than 230 energy researchers and industry representatives.

“We thought it would be a good idea to expand on the Battery Day idea and showcase a wide range of research and expertise in other areas, such as solar energy and clean fuels, in addition to what we’re doing in batteries and energy storage,” said Matt McDowell, associate professor in the George W. Woodruff School of Mechanical Engineering and the School of Materials Science and Engineering (MSE), and co-director, with Gleb Yushin, of the Advanced Battery Center.

Energy Materials Day will bring together experts from academia, government, and industry to discuss and accelerate research in three key areas: battery materials and technologies, photovoltaics and the grid, and materials for carbon-neutral fuel production, “all of which are crucial for driving the clean energy transition,” noted Eric Vogel, executive director of IMat and the Hightower Professor of Materials Science and Engineering.

“Georgia Tech is leading the charge in research in these three areas,” he said. “And we’re excited to unite so many experts to spark the important discussions that will help us advance our nation’s path to net-zero emissions.”

Building an Energy Hub

Energy Materials Day is part of an ongoing, long-range effort to position Georgia Tech, and Georgia, as a go-to location for modern energy companies. So far, the message seems to be landing. Georgia has had more than $28 billion invested or announced in electric vehicle-related projects since 2020. And Georgia Tech was recently ranked by U.S. News & World Report as the top public university for energy research.

Georgia has become a major player in solar energy, also, with the announcement last year of a $2.5 billion plant being developed by Korean solar company Hanwha Qcells, taking advantage of President Biden’s climate policies. Qcells’ global chief technology officer, Danielle Merfeld, a member of SEI’s External Advisory Board, will be the keynote speaker for Energy Materials Day.

“Growing these industry relationships, building trust through collaborations with industry — these have been strong motivations in our efforts to create a hub here in Atlanta,” said Yushin, professor in MSE and co-founder of Sila Nanotechnologies, a battery materials startup valued at more than $3 billion.

McDowell and Yushin are leading the battery initiative for Energy Materials Day and they’ll be among 12 experts making presentations on battery materials and technologies, including six from Georgia Tech and four from industry. In addition to the formal sessions and presentations, there will also be an opportunity for networking.

“I think Georgia Tech has a responsibility to help grow a manufacturing ecosystem,” McDowell said. “We have the research and educational experience and expertise that companies need, and we’re working to coordinate our efforts with industry.”

Marta Hatzell, associate professor of mechanical engineering and chemical and biomolecular engineering, is leading the carbon-neutral fuel production portion of the event, while Juan-Pablo Correa-Baena, assistant professor in MSE, is leading the photovoltaics initiative.

They’ll be joined by a host of experts from Georgia Tech and institutes across the country, “some of the top thought leaders in their fields,” said Correa-Baena, whose lab has spent years optimizing a semiconductor material for solar energy conversion.

“Over the past decade, we have been working to achieve high efficiencies in solar panels based on a new, low-cost material called halide perovskites,” he said. His lab recently discovered how to prevent the chemical interactions that can degrade it. “It’s kind of a miracle material, and we want to increase its lifespan, make it more robust and commercially relevant.”

While Correa-Baena is working to revolutionize solar energy, Hatzell’s lab is designing materials to clean up the manufacturing of clean fuels.

“We’re interested in decarbonizing the industrial sector, through the production of carbon-neutral fuels,” said Hatzell, whose lab is designing new materials to make clean ammonia and hydrogen, both of which have the potential to play a major role in a carbon-free fuel system, without using fossil fuels as the feedstock. “We’re also working on a collaborative project focusing on assessing the economics of clean ammonia on a larger, global scale.”

The hope for Energy Materials Day is that other collaborations will be fostered as industry’s needs and the research enterprise collide in one place — Georgia Tech’s Exhibition Hall — over one day. The event is part of what Yushin called “the snowball effect.”

“You attract a new company to the region, and then another,” he said. “If we want to boost domestic production and supply chains, we must roll like a snowball gathering momentum. Education is a significant part of that effect. To build this new technology and new facilities for a new industry, you need trained, talented engineers. And we’ve got plenty of those. Georgia Tech can become the single point of contact, helping companies solve the technical challenges in a new age of clean energy.”

The School of Physics’ new initiative to catalyze research using artificial intelligence (AI) and machine learning (ML) began October 16 with a conference at the Global Learning Center titled Revolutionizing Physics — Exploring Connections Between Physics and Machine Learning.

AI and ML have the spotlight right now in science, and the conference promises to be the first of many, says Feryal Özel, Professor and Chair of the School of Physics. 

"We were delighted to host the AI/ML in Physics conference and see the exciting rapid developments in this field,” Özel says. “The conference was a prominent launching point for the new AI/ML initiative we are starting in the School of Physics."​ 

That initiative includes hiring two tenure-track faculty members, who will benefit from substantial expertise and resources in artificial intelligence and machine learning that already exist in the Colleges of Sciences, Engineering, and Computing.

The conference attendees heard from colleagues about how the technologies were helping with research involving exoplanet searches, plasma physics experiments, and culling through terabytes of data. They also learned that a rough search of keyword titles by Andreas Berlind, director of the National Science Foundation’s Division of Astronomical Sciences, showed that about a fifth of all current NSF grant proposals include components around artificial intelligence and machine learning.

“That’s a lot,” Berlind told the audience. “It’s doubled in the last four years. It’s rapidly increasing.”

Berlind was one of three program officers from the NSF and NASA invited to the conference to give presentations on the funding landscape for AI/ML research in the physical sciences. 

“It’s tool development, the oldest story in human history,” said Germano Iannacchione, director of the NSF’s Division of Materials Research, who added that AI/ML tools “help us navigate very complex spaces — to augment and enhance our reasoning capabilities, and our pattern recognition capabilities.”

That sentiment was echoed by Dimitrios Psaltis, School of Physics professor and a co-organizer of the conference. 

“They usually say if you have a hammer, you see everything as a nail,” Psaltis said. “Just because we have a tool doesn't mean we're going to solve all the problems. So we're in the exploratory phase because we don't know yet which problems in physics machine learning will help us solve. Clearly it will help us solve some problems, because it's a brand new tool, and there are other instances when it will make zero contribution. And until we find out what those problems are, we're going to just explore everything.”

That means trying to find out if there is a place for the technologies in classical and modern physics, quantum mechanics, thermodynamics, optics, geophysics, cosmology, particle physics, and astrophysics, to name just a few branches of study.

Sanaz Vahidinia of NASA’s Astronomy and Astrophysics Research Grants told the attendees that her division was an early and enthusiastic adopter of AI and machine learning. She listed examples of the technologies assisting with gamma-ray astronomy and analyzing data from the Hubble and Kepler space telescopes. “AI and deep learning were very good at identifying patterns in Kepler data,” Vahidinia said. 

Some of the physicist presentations at the conference showed pattern recognition capabilities and other features for AI and ML: 

• Cassandra Hall, assistant professor of Computational Astrophysics at the University of Georgia, illustrated how machine learning helped in the search for hidden forming exoplanets. 
• Christopher J. Rozell, Julian T. Hightower Chair and Professor in the School of Electrical and Computer Engineering, spoke of his experiments using “explainable AI” (AI that conveys in human terms how it reaches its decisions) to track depression recovery with deep brain stimulation.
• Paulo Alves, assistant professor of physics at UCLA College of Physical Sciences Space Institute, presented on AI/ML as tools of scientific discovery in plasma physics.

Alves’s presentation inspired another physicist attending the conference, Psaltis said. “One of our local colleagues, who's doing magnetic materials research, said, ‘Hey, I can apply the exact same thing in my field,’ which he had never thought about before. So we not only have cross-fertilization (of ideas) at the conference, but we’re also learning what works and what doesn't.”

More information on funding and grants at the National Science Foundation can be found here. Information on NASA grants is found here. 

Wenjing Liao, an associate professor in the School of Mathematics, has been awarded a Department of Energy (DOE) Early Career Award for her research into how deep learning might be leveraging to make mathematical advances in achieving more efficient modeling techniques.

Liao was selected as one of the 93 early career scientists from across the country who are receiving a combined $135 million in DOE funding. The awards aim to support the next generation of STEM leaders, and identify early-career scientists whose research will have global impacts. 

Earlier this year, Liao was also selected for an National Science Foundation (NSF) Faculty Early Career Development Program (CAREER) Award, one of the most prestigious grants that a scientist can receive early in their profession. 

“Supporting America’s scientists and researchers early in their careers will ensure the U.S. remains at the forefront of scientific discovery and develops the solutions to our most pressing challenges,” said U.S. Secretary of Energy Jennifer M. Granholm, adding that the funding “will allow the recipients the freedom to find the answers to some of the most complex questions as they establish themselves as experts in their fields.”

Model simplification; complex problems

Real-world applications of computer modeling often call for large, complex data simulations, which can be time-consuming and expensive, limiting their applications. Liao’s project “Model Reduction by Deep Learning: Interpretability and Mathematical Advances” focuses on a technique called model reduction, which allows researchers to reduce the size of problems computer models must solve to smaller ones that computers can efficiently solve.

Liao notes that while traditional model-reduction methods have been successful, the technique is mostly limited to low dimensional linear models, or those with fewer important features that the model can include. However, many problems found in nature are the opposite. Liao hopes that by identifying the underlying nonlinear structures in natural problems, she can broaden the application of model-reduction techniques.

To do so, her research will focus on three key questions. First, she will investigate how to leverage deep neural networks to extract low-dimensional nonlinear structures in data sets. Next, Liao will investigate how to use the nonlinear structures in model reduction. Finally, in order to better harness deep learning, Liao aims to develop new deep learning-based model reduction methods.

“This project has the potential to drive significant advances in scientific machine learning,” Liao says in her abstract. “The proposed model-reduction methods can be used to analyze large datasets and simulate complex phenomena in physics, biology, and engineering.”