This fall, Assistant Professor Hua Chen received a three-year, $450,000 Early Career award from the National Science Foundation to support his theoretical work on Novel Electronic and Magnetic Dynamics and Responses in Non-collinear Magnetic Materials.
Assistant Professor Mike Mooney also received a five-year, $750,000 research award from the U.S. Department of Energy. His work will support experimental work related to the Deep Underground Neutrino Experiment at the Long-Baseline Neutrino Facility as well as the Short-Baseline Neutrino Program.
Read more about Mooney’s project here.
Chen’s project seeks to discover more about theoretical and computational research on non-collinear magnets. Non-collinear means the microscopic local magnetic moments in such magnets are neither parallel nor anti-parallel with one another. People have been more familiar with collinear magnets such as ferromagnets and anti-ferromagnets, but in recent years non-collinear magnets have shown interesting and advantageous properties. This is especially so in the field of spintronics, which studies the coupling between magnetic order and electric fields or currents.
The project will search for both new materials in the family of non-collinear magnets, and new response properties of them that do not have counterparts in conventional magnets. Both of the two objectives may lead to new device applications in information and energy technologies. His research group is excited to report any new findings arising from the project to the community in return.
Chen is inherently fascinated by the many unexplored possibilities in such unconventional magnetic systems. This award gives his research group both an external impetus and essential resource to focus on this interesting topic. Also exciting is the educational component of the project that includes a summer school on the topic of symmetry in magnetism, which will help to get junior participants in and outside of CSU ready for the challenges of the quantum era.
Additionally, Chen has developed an introductory computational materials science course in the materials science program of CSU. This aims to help graduate students with different backgrounds get a glimpse of the frontier of this field through hands-on Python coding and database usage. As an objective of the teaching component of the award, Chen will also enrich the content of the course by including an introduction to machine learning applied to materials science.
“I would like to sincerely thank my many colleagues at CSU, not only for helping me get this award, but also for the collegial and dynamic atmosphere that they collectively foster,” Chen said. “I am proud to be part of it.”