Mingda Li

Bio

Education

• PhD, Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 2015.
• BS, Department of Engineering Physics, Tsinghua University, China, 2009.

Appointment

• Summer researcher, MIT Energy Initiative, 2015.
• Postdoctoral Associate, Department of Mechanical Engineering, MIT, 2015-2017.

Awards

• Junior Bose Award, MIT School of Engineering, 2023
• U.S. Department of Energy (DOE) Early Career Research Program Award, 2021
• Ruth and Joel Spira Award for Distinguished Teaching, MIT School of Engineering, 2021

Research

The research focus of 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.

Publications

Selected Publications

NT Hung, R Okabe, A Chotrattanapituk and M Li, “Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures”
Advanced Materials 36, 2409175 (2024)

M Mandal, A Chotrattanapituk, K Woller, LJ Wu, H Xu, NT Hung, N Mao, R Okabe, A Boonkird, T Nguyen, NC Drucker, XM Chen, T Momiki, J Li, J Kong, Y Zhu, and M Li, “Precise Fermi-level engineering in a topological Weyl semimetal via fast ion implantation”
Applied Physics Review 11, 021429 (2024)

R Okabe, A Chotrattanapituk, A Boonkird, N Andrejevic, X Fu, TS Jaakkola, Q Song, T Nguyen, NC Drucker, S Mu, B Liao, Y Cheng, and M Li, “Virtual Node Graph Neural Network for Full Phonon Prediction”
Nature Computational Science 4, 522 (2024)

M Cheng, R Okabe, A Chotrattanapituk and M Li, “Machine Learning Detection of Majorana Zero Modes from Zero Bias Peak Measurements”
Matter 7, 2507 (2024)

R Okabe, S Xue, J Vavrek, J Yu, R Pavlovsky, V Negut, B Quiter, J Cates, T Liu, B Forget, S Jegelka, G Kohse, L-W Hu and M Li, “Tetris-inspired detector with neural network for radiation mapping”
Nature Communications 15, 3061 (2024)

NC Drucker, T Nguyen, F Han, P Siriviboon, X Luo, N Andrejevic, Z Zhu, G Bednik, QT Nguyen, Z Chen, LK Nguyen, T Liu, TJ Williams, MB Stone, AI Kolesnikov, S Chi, J Fernandez-Baca, C Nelson, A Alatas, T Hogan, AA Puretzky, S Huang, Y Yue and M Li “Topology stabilized fluctuations in a magnetic nodal semimetal”
Nature Communications 14, 5182 (2023)

Z Chen, X Shen, N Andrejevic, T Liu, D Luo, T Nguyen, NC Drucker, M Kozina, Q Song, C Hua, G Chen, X Wang, J Kong and M Li
“Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Machine Learning”
Advanced Materials 35, 2206997 (2023)

N Andrejevic, J Andrejevic, BA Bernevig, N Regnault, F Han, G Fabbris, T Nguyen, NC Drucker, CH Rycroft, and M Li
“Machine Learning Spectral Indicators of Topology”
Advanced Materials 34, 202204113 (2022)

N Andrejevic, Z. Chen, T Nguyen, L Fan, H Heiberger, LJ Zhou, YF Zhao, CZ Chang, A Grutter, and M Li
“Elucidating Proximity Magnetism through Polarized Neutron Reflectometry and Machine Learning”
Appl. Phys. Rev. 9, 011421 (2022)

T Nguyen, N Andrejevic, HC Po, Y Tsurimaki, NC Drucker, A Alatas, EE Alp, BM Leu, A Cunsolo, Y Cai, L Wu, JA Garlow, Y Zhu, H Lu, AC Gossard, AA Puretzky, DB Geohegan, S Huang and M Li
“Signature of Many-Body Localization of Phonons in Strongly Disordered Superlattices”
Nano Lett. 21, 7419 (2021)

Z Chen, N Andrejevic† NC Drucker, T Nguyen, RP Xian, T Smidt, Y Wang, R Ernstorfer, DA Tennant, M Chan, and M Li
“Machine Learning on Neutron and X-Ray Scattering and Spectroscopies”
Chem. Phys. Rev. 2, 031301 (2021)

Z Chen, N. Andrejevic, T Smidt, Z Ding, Q Xu, YT Chi, QT Nguyen, A Alatas, J Kong and M Li
“Direct Prediction of Phonon Density of States with Euclidean Neural Networks”
Advanced Science 8, 2004214 (2021)

F Han, N Andrejevic, T Nguyen, V Kozii, QT Nguyen, T Hogan, Z Ding, R Pablo-Pedro, S Parjan, B Skinner, A Alatas, EE Alp, S Chi, J Fernandez-Baca, S Huang, L Fu and M Li
“Quantized Thermoelectric Hall Effect Induces Giant Power Factor in a Topological Semimetal”
Nature Communications 11, 6167 (2020)

T Nguyen, F Han, N Andrejevic, R Pablo-Pedro, A Apte, Y Tsurimaki, Z Ding, K Zhang, A Alatas, EE Alp, S Chi, J Fernandez-Baca, M Matsuda, DA Tennant, Y Zhao, Z Xu, JW Lynn, S Huang and M Li
“Topological Singularity Induced Chiral Kohn Anomaly in a Weyl Semimetal”
Phys. Rev. Lett. 124, 236401 (2020)

Full list of publications can be found here.

Teaching

22.02 Introduction to Applied Nuclear Physics
22.12 Radiation Interactions, Control, and Measurement
22.042 Modeling with Machine Learning: Nuclear Science and Engineering Applications
22.S902 Quantum Theory of Materials Characterization

News

• A new approach to fine-tuning quantum materials
• AI method radically speeds predictions of materials’ thermal properties
• Mingda Li, one of two MIT teams selected for NSF sustainable materials grants
• With inspiration from “Tetris,” MIT researchers develop a better radiation detector
• Propelling atomically layered magnets toward green computers
• Researchers harness 2D magnetic materials for energy-efficient computing
• Physicists trap electrons in a 3D crystal for the first time
• Making more magnetism possible with topology
• MIT scholars awarded seed grants to probe the social implications of generative AI
• Mingda Li honored with Junior Bose Award for Excellence in Teaching
• MIT-led teams win National Science Foundation grants to research sustainable materials
• Seeing an elusive magnetic effect through the lens of machine learning
• A peculiar state of matter in layers of semiconductors
• Four MIT faculty members receive 2021 US Department of Energy early career awards
• A streamlined approach to determining thermal properties of crystalline solids and alloys
• A cool advance in thermoelectric conversion
• Newly observed phenomenon could lead to new quantum devices
• Mapping the effects of crystal defects
• Carbon nanotubes improve metal’s longevity under radiation
• Radiation physics today for materials science tomorrow
• Unusual magnetic behavior observed at a material interface
• Solving mysteries of conductivity in polymers
• Long-Sought Magnetic Mechanism Observed in Exotic Hybrid Materials