VCU Engineering’s Jayasimha Atulasimha, Ph.D., recently received two notable distinctions: the Richmond Joint Engineering Council (RJEC) Engineer of the Year for 2021 and the American Society of Mechanical Engineers (ASME) 2021 Adaptive Structures and Material Systems (ASMS) Active and Multifunctional Materials Outstanding Contribution Award.
“I like surprises and want to understand things I don’t know,” said Atulasimha, “but understanding is only the first step. Then you need to apply the new knowledge within industries that benefit from it. I am honored to receive these two distinguished awards and hope to continue my research to advance the fields of multifunctional materials and neuromorphic computing with my motivated students and collaborators, who I enjoy working with.”
Both awards were closely tied to a paper Atulasimha recently published in Nature Electronics titled “Creation and annihilation of nonvolatile fixed magnetic skyrmions using voltage control of magnetic anisotropy.” The paper focused on demonstrating how skyrmions could be used to create magnetic memory and computing devices by controlling them with electric fields rather than applying currents and magnetic fields. This provides a potential pathway for extremely energy efficient memory and neuromorphic computing devices.
This work was of particular interest to the ASME Active and Multifunctional Materials Technical Committee (AMM TC), which is part of the ASMS Division of ASME, where Atulasimha frequently presented before his research became more focused on nanoscale computing devices and skyrmions. Actuators and sensors are the typical application for multifunctional materials, but Atulasimha’s research on applications of multifunctional materials for computing devices goes beyond this traditional use. Atulasimha became an ASME Fellow in 2017 and worked closely with ASME ASMS helping to organize symposiums on multifunctional materials. His research contributions in the field earned him the Active and Multifunctional Materials Outstanding Contribution Award for 2021.
Atulasimha’s research was also important to the RJEC, which named him Engineer of the Year for 2021 for his work on nanoscale magnetic computing devices including the work on skyrmions. The organization promotes engineering education in the Richmond area by providing engineering-related information and speakers to Central Virginia schools through a volunteer coalition of scientists, engineers and technical professionals. Md Fahim Chowdhury, Atulasimha’s doctoral student, worked on deploying outreach modules on electronic gates with SNAP CIRCUIT kits developed in Atulasimha’s lab. This was part of an outreach program organized by VCU Mechanical and Nuclear Engineering Assistant Professor Radhika Barua, Ph.D. at Highland Springs High School to promote engineering education.
Other engineering outreach programs Atulasimha supports include the Math Science Innovations Center, which brings students to his lab for engineering workshops, and the Richmond Minorities in Engineering Program, where students work with CAD files and electron beam lithography to build microscopic magnets.
Atulasimha is a pioneer of “magnetic straintronics.” His research with voltage control of skyrmions builds from and also expands on this work by using direct (not strain mediated) electric field control of skyrmions. Magnetic straintronics employs electric-field-induced strain to implement energy-efficient nanomagnetic computing devices. This research has applications in both computer memory and neuromorphic computing that could enable embedded devices, like medical implants, to learn from data in real-time while consuming very little power.
For Atulasimha, commercialization of skyrmion and nanoscale magnetic devices research is the next step he is keen on pursuing with some of his Ph.D. students. He hopes the application of these discoveries will fuel further research in non-volatile memory and neuromorphic computing, leading to smaller devices that consume less energy and are more efficient for use in future hardware for artificial intelligence and machine learning.