Light emission – seemingly simple, but so much of our technology demands efficient, reliable light. Technologies like colloidal quantum dots and low-dimensional semiconductors are leading to high-performance light-emitting materials, from blue LEDs to ultraviolet-emitting perovskites. Machine learning, among other computational techniques, is used to cast a broader net in searching for material candidates.
Light Emitting Materials
In our group, we have combined the efforts of synthetic chemists, physicists, and engineers to design new light-emitting materials such as colloidal quantum dots and low-dimensional semiconductors. We investigate the surface chemistry of nanocrystal material to achieve promoted charge carrier mobility in the NC film as well as improved stability for luminescence. We are also interested in studying and regulating the crystal crystallization process during solution processing.
Our work has unveiled the basic physical and chemical principle properties of nanomaterials and has advanced the optoelectronic devices with high performance using these materials. We are also interested in using theoretical approaches and machine learning to predict and perfect new wide-bandgap, blue-emitting semiconductor materials.
Machine Learning Applications
Additionally, the rational design of new materials for optoelectronic applications benefits greatly from an initial computational screening of a large number of candidates using machine learning. In our group, we have combined the efforts of material computationalists and synthetic chemists, to identify the best material candidates for light-emitting applications. Following this strategy, we have successfully extended the breadth of metal halide perovskite applications beyond the visible spectral region with the discovery of new ultraviolet emitting perovskites.