Sander Cohen-Janes was chosen for the 2025 National Defense Science and Engineering Graduate Fellowship for his outstanding skills in advanced chemistry.
The prestigious fellowship, awarded by the Department of Defense (DoD), will provide Cohen-Janes with an annual stipend of $43,200, full tuition, and a travel budget to attend conferences for three years. It also includes opportunities for professional development and mentorship. The aim of the fellowship is to support talented individuals in making groundbreaking discoveries and innovations related to DoD interests.
Cohen-Janes is a 2nd-year graduate student in Professor Tianyu Zhu’s research group and specializes in theoretical chemistry and computational materials science.
“My work is all computational,” Cohen-Janes explains. “My project aims to drive advancements in the computational design and optimization of materials for high-performance quantum and sensing technologies.”
Key to this work is harnessing the unique properties of point defects, which are tiny imperfections in a material’s crystal lattice. Even though point defects might seem like small mistakes, they can have big effects on materials like semiconductors and insulators for quantum sensing and computing applications.
He explains, “When these crystals are disrupted at one point by removing an atom or switching it with something else that isn’t supposed to be there, it gives the material very different properties electronically. Then these point defects respond to external stimuli, like magnetic or electric fields.”
His research aims to design a framework for studying the excited states of point defect systems, which are hard to predict by existing computational methods due to cost and inaccuracies.
To address this, he plans to use a less common method called particle-particle random phase approximation (pp-RPA). He will enhance the pp-RPA method by implementing analytic nuclear gradients for excited states, allowing for accurate geometry calculations that other methods find challenging.
“Using this framework will generate high-quality data to train a machine learning model that can predict the parameters necessary to achieve specific sensitivities for solid-defect sensors.
Understanding the unique spectral properties of these defect systems can improve the use of these materials in quantum computing and sensing fields.”
Ultimately, his findings will be made available in an open-source platform for other chemists to use in their research.
Quantum sensors are used in a variety of applications, such as medical imaging, environmental monitoring, materials science, and defense applications. Their precise measurements make them valuable in detecting electronic emissions and fluctuations in gravitational fields, aiding in enhanced navigation, intelligence gathering, and threat detection in defense.
Learn more about the work done by the Zhu Group.