The researchers, along with Ray Osborn and Stephan Rosenkranz from the Materials Science Division, were recognized for their innovative approach in combining high-energy x-ray scattering, machine learning and spectral analysis, enabling powerful new methods of structural analysis to identify structural signatures of hidden order in spin-orbit coupled systems.
Electrons in quantum materials are known to interact with each other and, in certain cases, have exhibited so-called cooperative electron ordering that scientists believe should alter the material’s crystal structure. Traditional diffraction methods, however, have not been able to detect such changes.
To address this problem, the Argonne researchers, in collaboration with Cornell University, are developing a suite of machine learning tools that can identify multiple order parameters in x-ray scattering. The project, funded by the U.S. Department of Energy Office of Basic Energy Sciences, exploits recent advances in machine learning and improvements in the sensitivity of x-ray synchrotron sources.
By identifying the structural signatures of the hidden order in spin-orbit coupled materials, scientists hope to harness the interactions for quantum material applications, including smart sensors and actuators.
The PSE Excellence Awards celebrate individuals and teams for a range of superior accomplishments and contributions to Argonne’s PSE directorate. For a description of all the Argonne PSE winners, see this Argonne article.