Professor Bilge Yildiz’s research focuses on laying the scientific groundwork to enable next-generation electrochemical devices for energy conversion and information processing. She and her research-lab colleagues work on a variety of projects, all centered on the movement of charged atoms in materials. The scientific insights derived from her research guide the design of novel materials for brain-inspired energy-efficient computing, efficient and durable fuel cells, electrolytic water splitting, and solid-state batteries. Professor Yildiz’s approach combines computational and experimental analyses of electronic structure, defect mobility, and composition, using an in situ scanning probe and X-ray spectroscopy together with first-principles calculations and novel atomistic simulations.


Professor Yildiz received a BS in nuclear engineering from Hacettepe University in Ankara, Turkey, in 1999 and a PhD in nuclear science and engineering from MIT in 2003. She stayed at MIT to do postdoctoral research in electrochemistry and then moved to Argonne National Laboratory to investigate structure and chemistry of energy conversion materials using X-ray spectroscopy. She returned to MIT to join the faculty of the Department of Nuclear Science and Engineering and DMSE in 2007. Her research has been published in journals such as Nature Materials, Nature Nanotechnology, Nature Communications, Advanced Materials, and Science.

Key Publications

Nanosecond protonic programmable resistors for analog deep learning

Developed programmable resistors, or artificial synapses—devices that can be used to build analog deep learning processors. Compatible with silicon fabrication techniques, these artificial synapses increase the speed and reduce the energy needed to train neural network models.

Deep learning, a subset of artificial intelligence (AI), is key for successful automation, facilitating many analytical and computational tasks without human intervention. But training these models using current computers is associated with unsustainably high energy demand. Low-energy alternatives need to be found.

Deep learning processors that can execute computations fast while using much less energy can satisfy the growing need for AI while still meeting sustainability goals. Faster training of neural networks means faster deployment of deep learning use cases like fraud detection and medical imaging analysis.

Voltage control of ferrimagnetic order and voltage-assisted writing of ferrimagnetic spin textures

Used a small, externally applied voltage to manipulate the magnetic properties of ferrimagnetic materials without attendant structural damage.

Conventional data storage devices are limited by the unchangeable magnetic properties of ferromagnetic materials. Ferrimagnetic materials—with an ido respond to external forces. If we can switch the orientation of these magnets by 180 degrees, we can pack more data into a given space. But there has been no simple, fast, and reliable way of doing so.

Getting more data into less space could enable faster data storage, smaller sensors, and more efficient use of scarce raw materials.

Awards & Honors

Rahmi M. Koç Medal of Science, Koç University
Fellow, Royal Chemical Society
Fellow, American Physical Society
Ross Coffin Purdy Award, American Ceramic Society
Charles W. Tobias Young Investigator Award, Electrochemical Society