Research

Emergent Materials and Intelligent TEM (EMIT) Lab aims to advance the synthesis, characterization, and analysis of emergent functional materials via developing AI-driven combinational toolkit. Our research focuses on three interconnected areas:

1. Intelligent Synthesis: We develop automated robotic systems for high-throughput chemical synthesis and automated sample preparation for structural and functional characterization. Synthetic routes are dynamically optimized in real time based on AI-guided analysis of characterization data.

2. Intelligent Characterization: We develop intelligent transmission electron microscopy (TEM) techniques, including novel hardware for liquid-cell TEM and advanced data analysis algorithms for in situ and 4D TEM. These innovations enable us to uncover the fundamental structure-property relationships at the atomic scale..

3. Artificial Intelligence: We leverage AI techniques to design hierarchical materials, predict their structures and properties, and develop intelligent algorithms for analyzing complex characterization datasets, particularly from in situ and high-throughput experiments.



Multiscale hybridization of nanocrystals (e.g., quantum dots and nanosheets) and soft matrix (e.g., elastomers and liquid crystals), prepared through controllable self-assembly with emphasis on topology and strong electronic coupling, applications shown in the outer ring.
Combination of liquid-cell TEM and low-dose techniques enables the real-time in-situ imaging of synthesis and self-assembly of nanocrystals with atomic resolution and can also be used for e-beam sensitive materials such as soft matter and energy materials.
Artificial intelligence research in our lab consists of two interactive directions: developing algorithms to automatically identify materials structures from raw TEM data and optical spectra; developing learning networks to predict the products of chemistry in different environments.