Our group focuses on the study of local structure-property relationships of crystalline, non-crystalline, and nanocrystalline materials at the sub-nanometer length scale using advanced x-ray, neutron, and electron diffraction experiments. We develop the local structure characterization methodologies, such as atomic pair distribution function (PDF) and atomic electron tomography (AET) methods, utilizing some of the world’s most powerful scientific facilities, and applying advanced computation, including data mining and machine learning algorithms.

Prof. Long Yang is an Assistant Professor in the School of Materials Science and Engineering at Tongji University. He received his B.S. in Optics from Fudan University in 2015. After that, he earned his M.S. and Ph.D. degrees in Materials Science and Engineering from Columbia University in 2016 and 2021, respectively, under the supervision of Prof. Simon Billinge. Then he worked as a postdoctoral researcher in the group of Prof. Jianwei Miao at UCLA. In 2022, he joined Tongji Univesity as a tenure-track Assistant Professor.

Prof. Yang has published a series of papers in the fields of materials science and physics, and some chapters of a book published by Elsevier. He is one of the first committee members of the local structure and total scattering technique committee in the Chinese Crystallographic Society (IUCr, CCrS). He is also the leading developer for a series of scientific software in the community, including PDFitc (PDF in the cloud), ADDIE(ADvanced DIffraction Environment) and the new version of PDFgui.

Email: long_yang@tongji.edu.cn

Website: https://mif.tongji.edu.cn/info/1031/1707.htm

Office: Room 330, Decai Building, Tongji University, 4800 Caoan Road, Shanghai 201804, China.

Research Interests:

  1. Develop atomic local structure characterization techniques based on high-energy synchrotron x-ray, neutron, and electron diffraction experiments.
  2. Perform structure-property relationship studies on advanced materials in the fields of thermoelectric, energy-storage, and nanoscale functional materials.
  3. Improve advanced structure modeling methods using machine learning and data mining algorithms.

Position Availability:

  1. There are openings for masters and doctoral students who are interested in physics, materials science, and data science. No restrictions on their previous background.
  2. There are openings for scientific researchers (postdocs, assistant researchers, and research assistants) with background in physics, materials science, chemistry with high compensation.
  3. Undergraduate students are welcome to join and students are encouraged to publish their research results.
  4. Group members will frequently travel to and carry out experiments using the state-of-the-art scientific facilities at national laboratories over the world.
  5. If interested, please email Prof. Yang with your CV.