Skip to main content
MIT Corporate Relations
MIT Corporate Relations
Search
×
Read
Watch
Attend
About
Connect
MIT Startup Exchange
Search
Sign-In
Register
Search
×
MIT ILP Home
Read
Faculty Features
Research
News
Watch
Attend
Conferences
Webinars
Learning Opportunities
About
Membership
Staff
For Faculty
Connect
Faculty/Researchers
Program Directors
MIT Startup Exchange
User Menu and Search
Search
Sign-In
Register
MIT ILP Home
Toggle menu
Search
Sign-in
Register
Read
Faculty Features
Research
News
Watch
Attend
Conferences
Webinars
Learning Opportunities
About
Membership
Staff
For Faculty
Connect
Faculty/Researchers
Program Directors
MIT Startup Exchange
Back to Faculty/Researchers
Prof. Mingda Li
Class of 1947 Career Development Associate Professor of Nuclear Science and Engineering
Primary DLC
Department of Nuclear Science and Engineering
MIT Room:
24-209A
(617) 452-2505
mingda@mit.edu
https://web.mit.edu/nse/people/faculty/mli.html
Areas of Interest and Expertise
Machine Learning Materials
Materials Characterization and Diagnosis
Research Summary
The research focus of Professor Mingda and his group (Quantum Measurement Group) is to design novel materials characterization methods and to augment existing characterization methods to probe key properties of quantum materials that were either considered not measurable or not readily measurable with existing technique and analysis methods.
Materials characterization is essential for materials science. The birth of a new characterization method, such as X-ray diffraction (XRD), photoemission spectroscopy (PES), or inleastic neutron scattering (INS), all comes with great discoveries. However, the finite type of probe particles (e.g., photons, electrons, or neutrons) in one or more spaces (r, k, E, t) restricts the combination of measurable correlation functions, and even so, it is not always easy to interpret the experimental data.
To tackle the challenge, we take an integrated quantum theory, machine-learning, unconventional use of spectroscopies, and new architecture design approach: Quantum theory lays the foundation on measurable correlation functions, machine-learning aids to uncover hidden properties buried in data, unconventional use of neutron, x-ray, and electron spectra empowers existing techniques to a broader scope, and an integration of all these into new architecture can enable the detection of materials’ properties that evade experimental detection.
Recent Work
Projects
January 10, 2018
Department of Nuclear Science and Engineering
Energy Nano Group
Principal Investigator
Mingda Li
Related Faculty
Prof. Ju Li
Battelle Energy Alliance Professor of Nuclear Science and Engineering
Nestor Andres Sepulveda
Graduate Student
Alexandre N Guion
Graduate Student