Mingda Li

Mingda Li is an Associate Professor in the Department of Nuclear Science and Engineering at MIT. He earned his BS in Engineering Physics from Tsinghua University in 2009 and his PhD in NSE from MIT in 2015. He conducted postdoctoral research in MIT MechE. Li’s research focuses on Quantum, Nuclear, and AI.

Quantum

We build quantum theoretical frameworks to understand and predict emergent phenomena in complex quantum materials, with a focus on topological order, and materials with heterogeneities and defects. Our efforts combine effective field theory with advanced scattering and spectroscopic methods, including neutron, X-ray, and electron probes, to extract hidden quantum properties from experiments. We also help design next-generation measurement techniques to resolve fine quantum signatures. These insights inform the design of quantum materials for applications in microelectronics, quantum computing, and energy harvesting.

Energy

We investigate how energy carriers such as phonons, electrons, and other quasiparticles interact and dissipate energy, particularly at interfaces and under far-from-equilibrium conditions. Our approach combines nanoscale transport measurements, and state-of-the-art scattering techniques, and machine learning to uncover mechanisms of hot phonon relaxation, interfacial resistance, and directional heat flow. By understanding and controlling these processes, we design materials and interfaces with tailored energy dissipation, enabling improved thermal management in electronics, spintronics, and energy systems.

AI

We design machine learning and generative AI frameworks to accelerate the discovery of materials for energy and quantum technologies. By integrating high-throughput ab initio calculations with symmetry-aware neural networks, we identify candidates with exceptional thermal, electronic, and spintronic properties. These predictions are validated through iterative feedback with synthesis and characterization, enabling a closed-loop, data-driven pipeline for materials innovation. Our work supports the design of ultrafast energy devices and scalable quantum hardware, bridging materials discovery with device-level performance.